From 351d6d7c1b6c4c0ed372bdea7630c3a35d07b386 Mon Sep 17 00:00:00 2001 From: Jessica Mitchell Date: Tue, 16 Jan 2024 09:09:13 +0100 Subject: [PATCH 01/38] copy files from pynest examples, add toc --- doc/htmldoc/conf.py | 3 ++- doc/htmldoc/examples/index.rst | 2 ++ 2 files changed, 4 insertions(+), 1 deletion(-) diff --git a/doc/htmldoc/conf.py b/doc/htmldoc/conf.py index ee81b4a2f5..1de5d7b4a8 100644 --- a/doc/htmldoc/conf.py +++ b/doc/htmldoc/conf.py @@ -44,6 +44,7 @@ source_suffix = ".rst" master_doc = "index" extensions = [ + "add_button_notebook", "sphinx_gallery.gen_gallery", "list_examples", "sphinx.ext.autodoc", @@ -54,7 +55,6 @@ "sphinxcontrib.mermaid", "sphinx.ext.mathjax", "sphinxcontrib.plantuml", - "add_button_notebook", "IPython.sphinxext.ipython_console_highlighting", "nbsphinx", "extract_api_functions", @@ -80,6 +80,7 @@ "gallery_dirs": "auto_examples", "plot_gallery": "False", "download_all_examples": False, + "copyfile_regex": r".*\.rst|.*\.png", } # General information about the project. diff --git a/doc/htmldoc/examples/index.rst b/doc/htmldoc/examples/index.rst index b7977d0076..c87d8d03c0 100644 --- a/doc/htmldoc/examples/index.rst +++ b/doc/htmldoc/examples/index.rst @@ -341,7 +341,9 @@ PyNEST examples .. toctree:: :hidden: + :glob: + ../auto_examples/ignore_and_fire/* ../auto_examples/pong/run_simulations ../auto_examples/pong/pong ../auto_examples/pong/generate_gif From 34a0bc5ad46ab327e4ad1c562a81cf4cb618c01a Mon Sep 17 00:00:00 2001 From: Jessica Mitchell Date: Tue, 16 Jan 2024 09:10:05 +0100 Subject: [PATCH 02/38] add ignore_and_fire example files --- ...escription_TwoPopulationNetworkPlastic.rst | 580 ++++++++++++++ pynest/examples/ignore_and_fire/README.rst | 196 +++++ ...etworkPlastic_iaf_connectivity_postsim.png | Bin 0 -> 27212 bytes ...NetworkPlastic_iaf_connectivity_presim.png | Bin 0 -> 23287 bytes ...TwoPopulationNetworkPlastic_iaf_spikes.png | Bin 0 -> 487079 bytes ...etworkPlastic_iaf_weight_distributions.png | Bin 0 -> 26768 bytes ...etworkPlastic_ign_connectivity_postsim.png | Bin 0 -> 27800 bytes ...NetworkPlastic_ign_connectivity_presim.png | Bin 0 -> 23345 bytes ...TwoPopulationNetworkPlastic_ign_spikes.png | Bin 0 -> 486761 bytes ...etworkPlastic_ign_weight_distributions.png | Bin 0 -> 26813 bytes .../ignore_and_fire/figures/README.rst | 2 + .../generate_reference_data.py | 62 ++ .../generate_reference_figures.py | 181 +++++ pynest/examples/ignore_and_fire/model.py | 711 ++++++++++++++++++ .../ignore_and_fire/parameter_dicts.py | 79 ++ .../ignore_and_fire/reference_data/README.rst | 2 + pynest/examples/ignore_and_fire/scaling.png | Bin 0 -> 91904 bytes pynest/examples/ignore_and_fire/scaling.py | 309 ++++++++ .../ignore_and_fire/scaling/README.rst | 2 + .../ignore_and_fire/simulation_details.rst | 55 ++ 20 files changed, 2179 insertions(+) create mode 100644 pynest/examples/ignore_and_fire/ModelDescription_TwoPopulationNetworkPlastic.rst create mode 100644 pynest/examples/ignore_and_fire/README.rst create mode 100644 pynest/examples/ignore_and_fire/TwoPopulationNetworkPlastic_iaf_connectivity_postsim.png create mode 100644 pynest/examples/ignore_and_fire/TwoPopulationNetworkPlastic_iaf_connectivity_presim.png create mode 100644 pynest/examples/ignore_and_fire/TwoPopulationNetworkPlastic_iaf_spikes.png create mode 100644 pynest/examples/ignore_and_fire/TwoPopulationNetworkPlastic_iaf_weight_distributions.png create mode 100644 pynest/examples/ignore_and_fire/TwoPopulationNetworkPlastic_ign_connectivity_postsim.png create mode 100644 pynest/examples/ignore_and_fire/TwoPopulationNetworkPlastic_ign_connectivity_presim.png create mode 100644 pynest/examples/ignore_and_fire/TwoPopulationNetworkPlastic_ign_spikes.png create mode 100644 pynest/examples/ignore_and_fire/TwoPopulationNetworkPlastic_ign_weight_distributions.png create mode 100644 pynest/examples/ignore_and_fire/figures/README.rst create mode 100644 pynest/examples/ignore_and_fire/generate_reference_data.py create mode 100644 pynest/examples/ignore_and_fire/generate_reference_figures.py create mode 100644 pynest/examples/ignore_and_fire/model.py create mode 100644 pynest/examples/ignore_and_fire/parameter_dicts.py create mode 100644 pynest/examples/ignore_and_fire/reference_data/README.rst create mode 100644 pynest/examples/ignore_and_fire/scaling.png create mode 100644 pynest/examples/ignore_and_fire/scaling.py create mode 100644 pynest/examples/ignore_and_fire/scaling/README.rst create mode 100644 pynest/examples/ignore_and_fire/simulation_details.rst diff --git a/pynest/examples/ignore_and_fire/ModelDescription_TwoPopulationNetworkPlastic.rst b/pynest/examples/ignore_and_fire/ModelDescription_TwoPopulationNetworkPlastic.rst new file mode 100644 index 0000000000..81a742cef5 --- /dev/null +++ b/pynest/examples/ignore_and_fire/ModelDescription_TwoPopulationNetworkPlastic.rst @@ -0,0 +1,580 @@ +.. _sec_model_description: + +Two population STDP network model description +============================================= + +Summary of network model +------------------------ + +.. list-table:: + + + * - Populations + - excitatory population :math:`\mathcal{E}` inhibitory population :math:`\mathcal{I}`, external Poissonian spike sources :math:`\mathcal{X}` + * - Connectivity + - sparse random connectivity respecting Dale’s principle + * - Neurons + - leaky integrate-and-fire (LIF) + * - Synapses + - linear input integration with alpha-shaped postsynaptic currents (PSCs), spike-timing dependent plasticity (STDP) for connections between + excitatory neurons + * - Input + - stationary, uncorrelated Poissonian spike trains + + +Detailed desciption of network model +------------------------------------ + +Populations +~~~~~~~~~~~ + +.. table:: + + +---------------------+----------------------+----------------------------------+ + | **Name** | **Elements** | **Size** | + +=====================+======================+==================================+ + | :math:`\mathcal{E}` | LIF neurons | :math:`N_\text{E}=\beta{}N` | + | | | | + +---------------------+----------------------+----------------------------------+ + | :math:`\mathcal{I}` | LIF neurons | :math:`N_\text{I}=N-N_\text{E}` | + | | | | + +---------------------+----------------------+----------------------------------+ + | :math:`\mathcal{X}` | realizations of a | :math:`N` | + | | Poisson point | | + | | process | | + +---------------------+----------------------+----------------------------------+ + +Connectivity +~~~~~~~~~~~~ + +.. table:: + + +---------------------+----------------------+---------------------------------------------------------+ + | **Source** | **Target** | **Pattern** | + +=====================+======================+=========================================================+ + | :math:`\mathcal{E}` | :math:`\mathcal{E}` | - random, | + | | | independent; | + | | | homogeneous | + | | | in-degree | + | | | :math:`K_{\text{E},i}=K_\text{E}` | + | | | | + | | | (:math:`\forall{}i\in\mathcal{E}`) | + | | | | + | | | | + | | | - plastic synaptic weights | + | | | :math:`J_{ij}(t)` | + | | | | + | | | (:math:`\forall{}i\in\mathcal{E},j\in\mathcal{E}`) | + | | | | + | | | - homogeneous | + | | | | + | | | spike-transmission | + | | | delays | + | | | :math:`d_{ij}=d` | + | | | | + | | | (:math:`\forall{}i\in\mathcal{E},j\in\mathcal{E}`) | + | | | | + +---------------------+----------------------+---------------------------------------------------------+ + | :math:`\mathcal{E}` | :math:`\mathcal{I}` | - random, | + | | | independent; | + | | | homogeneous | + | | | in-degree | + | | | :math:`K_{\text{E},i}=K_\text{E}` | + | | | | + | | | (:math:`\forall{}i\in\mathcal{I}`) | + | | | | + | | | | + | | | - fixed synaptic | + | | | weights | + | | | :math:`J_{ij}\in\{0,J\}` | + | | | | + | | | (:math:`\forall{}i\in\mathcal{I},j\in\mathcal{E}`) | + | | | | + | | | | + | | | | + | | | - homogeneous | + | | | | + | | | spike-transmission | + | | | delays | + | | | :math:`d_{ij}=d` | + | | | | + | | | (:math:`\forall{}i\in\mathcal{I},j\in\mathcal{E}`) | + | | | | + +---------------------+----------------------+---------------------------------------------------------+ + | :math:`\mathcal{I}` | :math:`\mathcal \ | - random, | + | | {E}\cup\mathcal{I}` | independent; | + | | | homogeneous | + | | | in-degree | + | | | :math:`K_{\text{I},i}=K_\text{I}` | + | | | | + | | | (:math:`forall{}i\in\mathcal{E}\cup\mathcal{I}`)\ | + | | | j\in\mathcal{I}`) | + | | | | + | | | - fixed synaptic | + | | | weights | + | | | :math:`J_{ij}\in\{-gJ,0\}` | + | | | | + | | | (:math:`\forall{}i\in\mathcal{E}\cup\mathcal{I}, \ | + | | | j\in\mathcal{I}`) | + | | | | + | | | | + | | | - homogeneous | + | | | | + | | | spike-transmission | + | | | delays | + | | | :math:`d_{ij}=d` | + | | | | + | | | (:math:`\forall{}i\in\mathcal{E}\cup\mathcal{I}, \ | + | | | j\in\mathcal{I}`) | + | | | | + | | | | + +---------------------+----------------------+---------------------------------------------------------+ + | :math:`\mathcal{X}` | :math:`\mathcal \ | - one-to-one | + | | {E}\cup\mathcal{I}` | | + | | | - fixed synaptic | + | | | weights | + | | | :math:`J_{ij}=J` | + | | | | + | | | (:math:`\forall{}i\in\mathcal{E}\cup\mathcal{I}, \ | + | | | j\in\mathcal{X}`) | + | | | | + | | | - homogeneous | + | | | | + | | | spike-transmission | + | | | delays | + | | | :math:`d_{ij}=d` | + | | | | + | | | (:math:`\forall{}i\in\mathcal{E}\cup\mathcal{I}, \ | + | | | j\in\mathcal{X}`) | + | | | | + +---------------------+----------------------+---------------------------------------------------------+ + + + +Neuron: Leaky integrate-and-fire (LIF) dynamics +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + + +Dynamics of membrane potential :math:`V_{i}(t)` and +spiking activity :math:`s_i(t)` of neuro :math:`i\in\left\{1,\ldots,N\right\}`: + +emission of :math:`k`\ th (:math:`k=1,2,\ldots`) spike of neuron +:math:`i` at time :math:`t_{i}^{k}` if + +.. math:: + V_{i}\left(t_{i}^{k}\right)\geq\theta + +with spike threshold :math:`\theta` + + - reset and refractoriness: + +.. math:: \forall{}k,\ \forall t \in \left(t_{k}^{i},\,t_{k}^{i}+\tau_\text{ref}\right]:\quad V_{i}(t)=V_\text{reset} + +with refractory period :math:`\tau_\text{ref}` and reset potential +:math:`V_\text{reset}` + +- spike train :math:`\displaystyle s_i(t)=\sum_k \delta(t-t_i^k)` + +- subthreshold dynamics of membrane potential :math:`V_{i}(t)`: + +.. math:: + + \begin{aligned} + &\forall{}k,\ \forall t \notin \left[t_{i}^{k},\,t_{i}^{k}+\tau_\text{ref}\right):\\ + &\qquad\tau_\text{m}\frac{\text{d}{}V_i(t)}{\text{d}{}t} = + \Bigl[E_\text{L}-V_i(t)\Bigr]+R_\text{m}I_i(t) + \end{aligned} + +with membrane time constant :math:`\tau_\text{m}`, membrane +resistance :math:`R_\text{m}`, resting potential :math:`E_\text{L}`, +and total synaptic input current :math:`I_i(t)` + + +Synapse: transmission +~~~~~~~~~~~~~~~~~~~~~ + +Current-based synapses with alpha-function shaped postsynaptic currents (PSCs) +`````````````````````````````````````````````````````````````````````````````` + + +Total synaptic input current of neuron :math:`i` + +.. math:: I_i(t)=I_{\text{E},i}(t)+I_{\text{I},i}(t)+I_{\text{X},i}(t) + +- excitatory, inhibitory and external synaptic input currents + +.. math:: + + %I_{P,i}(t)=\sum_{j\in\mathcal{P}}(\text{PSC}_{ij}*s_j)(t) + %\quad\text{for}\quad + %(P,\mathcal{P})\in\{(\exc,\Epop),(\inh,\Ipop),(\ext,\Xpop)\} + %, + \begin{aligned} + I_{\text{E},i}(t)&=\sum_{j\in\mathcal{E}}\bigl(\text{PSC}_{ij}*s_j\bigr)(t-d_{ij})\\ + I_{\text{I},i}(t)&=\sum_{j\in\mathcal{I}}\bigl(\text{PSC}_{ij}*s_j\bigr)(t-d_{ij})\\ + I_{\text{X},i}(t)&=\sum_{j\in\mathcal{X}}\bigl(\text{PSC}_{ij}*s_j\bigr)(t-d_{ij}) + \end{aligned} + +with spike trains :math:`s_j(t)` of local +(:math:`j\in\mathcal{E}\cup\mathcal{I}`) and external sources +(:math:`j\in\mathcal{X}`), spike transmission delays :math:`d_{ij}`, +and convolution operator “:math:`*`”: +:math:`\displaystyle\bigl(f*g\bigr)(t)=\int_{-\infty}^\infty\text{d}s\,f(s)g(t-s)`) + +- alpha-function shaped postsynaptic currents + +.. math:: \text{PSC}_{ij}(t)=\hat{I}_{ij}e\tau_\text{s}^{-1}te^{-t/\tau_\text{s}}\Theta(t) + +with synaptic time constant :math:`\tau_\text{s}` and Heaviside +function :math:`\Theta(\cdot)` + +- postsynaptic potential triggered by a single presynaptic spike + +.. math:: + + \text{PSP}_{ij}(t)= + \hat{I}_{ij}\frac{e}{\tau_\text{s}C_\text{m}} + \left(\frac{1}{\tau_\text{m}}-\frac{1}{\tau_\text{s}}\right)^{-2} + \left(\left(\frac{1}{\tau_\text{m}}-\frac{1}{\tau_\text{s}}\right) t e^{-t/\tau_\text{s}} - e^{-t/\tau_\text{s}} + e^{-t/\tau_\text{m}} \right) \Theta(t) + +- PSC amplitude (synaptic weight) + +.. math:: + + \hat{I}_{ij}=\text{max}_t\bigl(\text{PSC}_{ij}(t)\bigr) + =\frac{J_{ij}}{J_\text{unit}(\tau_\text{m},\tau_\text{s},C_\text{m})} + +parameterized by PSP amplitude +:math:`J_{ij}=\text{max}_t\bigl(\text{PSP}_{ij}(t)\bigr)` + +- with unit PSP amplitude (PSP amplitude for :math:`\hat{I}_{ij}=1`): + + .. math:: + + J_\text{unit}(\tau_\text{m},\tau_\text{s},C_\text{m}) + = \frac{e}{C_\text{m}\left(1-\frac{\tau_\text{s}}{\tau_\text{m}}\right)}\left( \frac{e^{-t_\text{max}/\tau_\text{m}} - e^{-t_\text{max}/\tau_\text{s}}}{\frac{1}{\tau_\text{s}} - \frac{1}{\tau_\text{m}}} - t_\text{max}e^{-t_\text{max}/\tau_\text{s}} \right), + +- time to PSP maximum + + .. math:: + + t_\text{max} = + \frac{1}{\frac{1}{\tau_\text{s}} - \frac{1}{\tau_\text{m}}}\left(-W_{-1}\left(\frac{-\tau_\text{s}e^{-\frac{\tau_\text{s}}{\tau_\text{m}}}}{\tau_\text{m}}\right) - \frac{\tau_\text{s}}{\tau_\text{m}}\right), + + and Lambert-W function :math:`\displaystyle W_{-1}(x)` for + :math:`\displaystyle x \ge -1/e` + + + +Synapse: plasticity +~~~~~~~~~~~~~~~~~~~ + +Spike-timing dependent plasticity (STDP) with power-law weight dependence and all-to-all spike pairing scheme +````````````````````````````````````````````````````````````````````````````````````````````````````````````` + +See Morrison et al. [1]_ for connections between excitatory neurons. + + +Dynamics of synaptic weights :math:`J_{ij}(t)` :math:`\forall{}i\in\mathcal{E}, j\in\mathcal{E}`: + + .. math:: + + \begin{aligned} + &\forall J_{ij}\ge{}0: \\[1ex] + &\quad + \frac{\text{d}}{}J_{ij}{\text{d}{}t}= + \lambda^+f^+(J_{ij})\sum_k x^+_j(t)\delta\Bigl(t-[t_i^k+d_{ij}]\Bigr) + + \lambda^-f^-(J_{ij})\sum_l x^-_i(t)\delta\Big(t-[t_j^l-d_{ij}]\Bigr)\\[1ex] + &\forall{}\{t|J_{ij}(t)<0\}: \quad J_{ij}(t)=0 \quad \text{(clipping)} + \end{aligned} + + with + + - pre- and postsynaptic spike times :math:`\{t_j^l|l=1,2,\ldots\}` and + :math:`\{t_i^k|k=1,2,\ldots\}`, + + - magnitude :math:`\lambda^+=\lambda` of weight update for causal + firing (postsynaptic spike following presynaptic spikes: + :math:`t_i^k>t_j^l`), + + - magnitude :math:`\lambda^-=-\alpha\lambda` of weight update for + acausal firing (presynaptic spike following postsynaptic spikes: + :math:`t_i^k` + +- :doc:`Parameter dictionary ` + +- :doc:`Simulation details ` + + +- :doc:`Detailed network model description and + parameters ` + + +- :doc:`Run scaling experiments and generate scaling + figure ` + +- :doc:`Run network simulations to produce spike and synaptic-weight + data shown in figures below ` + +- :doc:`Generate figure below showing spiking activity and synaptic weight + distributions ` + + +Citing this code +---------------- + +If you use this code, we ask you to cite the paper by ??? and the NEST release on Zenodo. + + +(Non-) Scalability of recurrent neuronal networks +------------------------------------------------- + +The verification and validation of simulation architectures (soft- and +hardware) is typically based on models describing networks of neurons. +Ideally, such test-case models are *scalable* with respect to the +network size + +- to foster a comparison between different computing architectures with + different computational resources, +- to be able to extrapolate (up-scale) to networks at brain scale, even + if data constrained and well tested network models at this scale are + not published or existing yet, and +- to be able to study and compare different plasticity mechanisms with + slow dynamics (down-scaling). + +Biological neuronal networks are characterized by a high degree of +recurrency. As shown by van Albada et al. (2015), scaling the number of +nodes or edges in a recurrent neuronal networks generally alters the +network dynamics such as the structure of pairwise correlations. +Preserving certain dynamical features by adjusting other parameters can +only be achieved in limited ranges or exceptional cases. Recurrent +neuronal networks are hence not truly scalable. In this example, we +demonstrate how the ``ignore_and_fire`` can help to perform +systematic scaling experiments with arbitrary types of networks, without +altering the network dynamics. + +Network model +------------- + +In this example, we employ a simple network model describing the +dynamics of a local cortical circuit at the spatial scale of ~1mm within +a single cortical layer. It is derived from the model proposed in +(Brunel [1]_), but accounts for the synaptic weight dynamics for +connections between excitatory neurons. The weight dynamics are +described by the spike-timing-dependent plasticity (STDP) model derived +by Morrison et al. ( [7]_). The model provides a mechanism underlying the +formation of broad distributions of synaptic weights in combination with +asynchronous irregular spiking activity (see figure below). A detailed +description of the network model and parameters are provided +`here `__. +Implementation details can be found +`here `__. + +A variant of this model, the +:doc:`hpc_benchmark ` +has been used in a number of benchmarking studies, in particular for +weak-scaling experiments (Helias et al., [2]_; Kunkel et al., [3]_; +Ippen et al., [4]_; Kunkel & Schenk, [5]_; Jordan et al., [6]_). Due to +its random homogeneous connectivity, the model represents a hard +benchmarking scenario: each neuron projects with equal probability to +any other neuron in the network. Implementations of this model can +therefore not exploit any spatial connectivity patterns. In contrast to +the model used here, the plasticity dynamics in the ``hpc_benchmark`` is +parameterized such that it has only a weak effect on the synaptic +weights and, hence, the network dynamics. Here, the effect of the +synaptic plasticity is substantial and leads to a significant broadening +of the weight distribution (see figure below). Synaptic weights thereby +become a sensitive target metric for verification and validation +studies. + +Comparison between the networks with ``integrate-and-fire`` and ``ignore-and-fire`` dynamics +-------------------------------------------------------------------------------------------- + +The model employed here can be configured into a truly scalable mode by +replacing the integrate-and-fire neurons by an ``ignore_and_fire`` +dynamics (for details, see +`here `__). By +doing so, the spike generation dynamics is decoupled from the input +integration and the plasticity dynamics; the overall network activity, +and, hence, the communication load, is fully controlled by the user. The +firing rates and phases of the ``ignore-and-fire`` model are randomly +drawn from uniform distributions to guarantee asynchronous spiking +activity. The plasticity dynamics remains intact (see figure below). + +================== ===================== +``iaf_psc_alpha`` ``ignore_and_fire`` +================== ===================== +|iaf_spikes| |ign_spikes| +|iaf_weight| |ign_weight| +================== ===================== + + +.. |iaf_spikes| image:: TwoPopulationNetworkPlastic_iaf_spikes.png +.. |iaf_weight| image:: TwoPopulationNetworkPlastic_iaf_weight_distributions.png +.. |ign_spikes| image:: TwoPopulationNetworkPlastic_ign_spikes.png +.. |ign_weight| image:: TwoPopulationNetworkPlastic_ign_weight_distributions.png + + +Spiking activity (top) and synaptic weight distributions (bottom) of +the network with integrate-and-fire (``iaf_psc_alpha_nest``) and +ignore-and-fire dynamics (``ignore_and_fire``). Scaling experiments + +------------------- + +The ``ignore_and_fire`` variant of the model permits exact scaling +experiments, without the need for any parameter tuning when changing the +network size (see figure below). + + +.. figure:: scaling.png + :scale: 50% + + Dependence of the simulation time (top), the time and population + averaged firing rate (middle) and the synaptic weights (bottom) on the + network size :math:`N` for the\ ``integrate-and-fire`` (black) and the + ``ignore-and-fire`` variant of the network model (gray). The in-degree + :math:`K=1250` is fixed. + + +References +---------- + +.. [1] Brunel (2000). Dynamics of networks of randomly connected excitatory + and inhibitory spiking neurons. Journal of Physiology-Paris + 94(5-6):445-463. + doi:10.1023/A:1008925309027 + +.. [2] Helias M, Kunkel S, Masumoto G, Igarashi J, Eppler JM, Ishii S, Fukai + T, Morrison A, Diesmann M (2012). Supercomputers ready for use as + discovery machines for neuroscience. 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z8aVt?b_}dQED6xYzK(}AzJx8!3~%^b zk}Xllsj!pbhk`Gl2rJqoi8h#|OLnxSP8JIcKu3}jnw*B-$`S=Ja$PZHVU<>H6t&^7 z*Hh_UcL|$|5@+ptX|GvS&Zx-+&P1#s;+9zc$$L$31M;9BZrxhJZ}a=0+6^1(yFMr@ zxOG= time_interval[0]) * (spikes[:, 1] <= time_interval[1])) + rate = n_events / D * 1000.0 / pop_size + print("\n-----> average firing rate: nu=%.2f /s\n" % (rate)) + return rate + + +################################################# +def plot_spikes(spikes, nodes, pars, path="./"): + """ + Create raster plot of spiking activity. + """ + + pop_all = np.array(nodes["pop_all"]) + + rate = time_and_population_averaged_spike_rate(spikes, (0.0, pars["T"]), pars["N_rec_spikes"]) + + # plot spiking activity + plt.figure(num=1, figsize=(7, 5)) + plt.clf() + plt.title(r"time and population averaged firing rate: $\nu=%.2f$ spikes/s" % rate) + plt.plot(spikes[:, 1], spikes[:, 0], "o", ms=0.5, lw=0, mfc="k", mec="k", alpha=0.3, rasterized=True) + + plt.xlim(0, pars["T"]) + plt.ylim(0, pars["N_rec_spikes"]) + plt.xlabel(r"time (ms)") + plt.ylabel(r"neuron id") + plt.savefig(path + "/TwoPopulationNetworkPlastic_spikes.png") + + +# def center_axes_ticks(ax): +# """ +# Shift axes ticks in matrix plots to the center of each box. +# """ +# xticks = ax.get_xticks() +# dx=(xticks[1]-xticks[0])/2. +# ax.set_xticks(xticks+dx) +# ax.set_xticklabels(xticks.astype("int")) + +# yticks = ax.get_yticks() +# dy=(yticks[1]-yticks[0])/2. +# ax.set_yticks(yticks+dy) +# ax.set_yticklabels(yticks.astype("int")) + + +################################################# +def plot_connectivity_matrix(W, pop_pre, pop_post, filename_label=None, path="./"): + """ + Plot connectivity matrix "W" for source and target neurons contained in "pop_pre" and "pop_post", + and save figure to file. + """ + + wmin = 0 + wmax = 150 + + print("\nPlotting connectivity matrix...") + fig = plt.figure(num=2, figsize=(6, 5)) + plt.clf() + gs = gridspec.GridSpec(1, 2, width_ratios=[15, 1]) + + ### + + matrix_ax = fig.add_subplot(gs[0]) + cmap = plt.cm.gray_r + matrix = plt.pcolor(pop_pre, pop_post, W, cmap=cmap, rasterized=True, vmin=wmin, vmax=wmax) + plt.xlabel(r"source id") + plt.ylabel(r"target id") + + # center_axes_ticks(matrix_ax) + + plt.xlim(pop_pre[0], pop_pre[-1]) + plt.ylim(pop_post[0], pop_post[-1]) + + ### + + cb_ax = plt.subplot(gs[1]) + cb = plt.colorbar(matrix, cax=cb_ax, extend="max") + cb.set_label(r"synaptic weight (pA)") + + ### + + plt.subplots_adjust(left=0.12, bottom=0.1, right=0.9, top=0.95, wspace=0.1) + plt.savefig(path + "/TwoPopulationNetworkPlastic_connectivity%s.png" % (filename_label)) + + +################################################# +def plot_weight_distributions(whist_presim, whist_postsim, weights, path="./"): + """ + Plot distributions of synaptic weights before and after simulation. + """ + print("\nPlotting weight distributions...") + fig = plt.figure(num=3, figsize=(6, 4)) + plt.clf() + lw = 3 + clr = ["0.6", "0.0"] + plt.plot(weights[:-1], whist_presim, lw=lw, color=clr[0], label=r"pre sim.") + plt.plot(weights[:-1], whist_postsim, lw=lw, color=clr[1], label=r"post sim.") + # plt.setp(plt.gca(),xscale="log") + plt.setp(plt.gca(), yscale="log") + plt.legend(loc=1) + plt.xlabel(r"synaptic weight (pA)") + plt.ylabel(r"rel. frequency") + plt.xlim(weights[0], weights[-2]) + plt.ylim(5e-5, 3) + plt.subplots_adjust(left=0.12, bottom=0.12, right=0.95, top=0.95) + plt.savefig(path + "/TwoPopulationNetworkPlastic_weight_distributions.png") + + +################################################# +def generate_reference_figures(): + """Generate and store set of reference data""" + + ## raster plot + parameters = model.get_default_parameters() + parameters["record_spikes"] = True + parameters["record_weights"] = True + + ## fetch node ids + model_instance = model.Model(parameters) + model_instance.create() + + ## create subfolder for figures (if necessary) + os.system("mkdir -p " + model_instance.pars["data_path"]) + + ## load spikes from reference data + spikes = model.load_spike_data( + "../reference_data/" + model_instance.pars["data_path"], + "spikes-%d" % (np.array(model_instance.nodes["spike_recorder"])[0]), + ) + plot_spikes(spikes, model_instance.nodes, model_instance.pars, model_instance.pars["data_path"]) + + ## load connectivity from reference data + connectivity_presim = model.load_connectivity_data( + "../reference_data/" + model_instance.pars["data_path"], "connectivity_presim" + ) + connectivity_postsim = model.load_connectivity_data( + "../reference_data/" + model_instance.pars["data_path"], "connectivity_postsim" + ) + + ## create connectivity matrices before and after simulation for a subset of neurons + subset_size = 100 + pop_pre = np.array(model_instance.nodes["pop_E"])[:subset_size] + pop_post = np.array(model_instance.nodes["pop_E"])[:subset_size] + W_presim, pop_pre, pop_post = model.get_connectivity_matrix(connectivity_presim, pop_pre, pop_post) + W_postsim, pop_pre, pop_post = model.get_connectivity_matrix(connectivity_postsim, pop_pre, pop_post) + + ## plot connectivity matrices + plot_connectivity_matrix(W_presim, pop_pre, pop_post, "_presim", model_instance.pars["data_path"]) + plot_connectivity_matrix(W_postsim, pop_pre, pop_post, "_postsim", model_instance.pars["data_path"]) + + ## compute weight distributions + # weights = np.arange(29.5,34.1,0.05) + weights = np.arange(0.0, 150.1, 0.5) + whist_presim = model.get_weight_distribution(connectivity_presim, weights) + whist_postsim = model.get_weight_distribution(connectivity_postsim, weights) + + ## plot weight distributions + plot_weight_distributions(whist_presim, whist_postsim, weights, model_instance.pars["data_path"]) + + +################################################# + +generate_reference_figures() diff --git a/pynest/examples/ignore_and_fire/model.py b/pynest/examples/ignore_and_fire/model.py new file mode 100644 index 0000000000..0b7928bb14 --- /dev/null +++ b/pynest/examples/ignore_and_fire/model.py @@ -0,0 +1,711 @@ +""" +Two population STDP network model +--------------------------------- + +In this example, we employ a simple network model describing the +dynamics of a local cortical circuit at the spatial scale of ~1mm within +a single cortical layer. It is derived from the model proposed in +(Brunel [1]_), but accounts for the synaptic weight dynamics for +connections between excitatory neurons. The weight dynamics are +described by the spike-timing-dependent plasticity (STDP) model derived +by Morrison et al. ( [7]_). The model provides a mechanism underlying the +formation of broad distributions of synaptic weights in combination with +asynchronous irregular spiking activity (see figure below). A detailed + + +The model used here can be configured into a truly scalable mode by +replacing the integrate-and-fire neurons by an ``ignore_and_fire`` +dynamics. +By doing so, the spike generation dynamics is decoupled from the input +integration and the plasticity dynamics; the overall network activity, +and, hence, the communication load, is fully controlled by the user. The +firing rates and phases of the ``ignore-and-fire`` model are randomly +drawn from uniform distributions to guarantee asynchronous spiking +activity. The plasticity dynamics remains intact. + +Default parameters are provided in "parameter_dicts.py". +For more information see :doc:`/auto_examples/ignore_and_fire/index`. + +""" + +import copy +import os +import sys + +import nest +import numpy as np +import scipy.special + +############################################## +# Instantiation of the TwoPopulationNetworkPlastic model and its PyNEST implementation. +# + + +class Model: + """ + Instatitation of model + """ + + def __init__(self, parameters): + """ + Intialise model and simulation instance, including + + 1) parameter setting, + 2) configuration of the NEST kernel, + 3) setting random-number generator seed, and + 4) configuration of neuron and synapse models. + + Arguments + --------- + + parameters: dict + Parameter dictionary + + Returns + ------- + + """ + + print("\nInitialising model and simulation...") + + # set parameters derived from base parameters + self.__derived_parameters(parameters) + + # create data directory (if necessary) + os.system("mkdir -p " + self.pars["data_path"]) + + # initialize NEST kernel + nest.ResetKernel() + nest.SetKernelStatus( + { + "tics_per_ms": 1.0 * self.pars["tics_per_step"] / self.pars["dt"], + "resolution": self.pars["dt"], + "print_time": self.pars["print_simulation_progress"], + "local_num_threads": self.pars["n_threads"], + "rng_seed": self.pars["seed"], + "dict_miss_is_error": True, + "data_path": self.pars["data_path"], + "overwrite_files": True, + } + ) + np.random.seed(self.pars["seed"]) + + nest.set_verbosity(self.pars["nest_verbosity"]) + + # configure neuron and synapse models + if self.pars["neuron_model"] == "iaf_psc_alpha": + self.__neuron_params = { + "V_th": self.pars["theta"], + "E_L": self.pars["E_L"], + "V_reset": self.pars["V_reset"], + "tau_m": self.pars["tau_m"], + "t_ref": self.pars["t_ref"], + "C_m": self.pars["C_m"], + "tau_syn_ex": self.pars["tau_s"], + "tau_syn_in": self.pars["tau_s"], + "I_e": self.pars["I_DC"], + "tau_minus": self.pars["stdp_tau_minus"], + "V_m": self.pars["V_init_min"], + } + elif self.pars["neuron_model"] == "ignore_and_fire": + self.__neuron_params = {} + + ############################################## + def __derived_parameters(self, parameters): + """ + Set additional parameters derived from base parameters. + + A dictionary containing all (base and derived) parameters is stored as model attribute self.pars. + + Arguments + --------- + + parameters: dict + Dictionary containing base parameters + + """ + + self.pars = copy.deepcopy(parameters) + + self.pars["N_E"] = int(self.pars["beta"] * self.pars["N"]) # number of excitatory neurons + self.pars["N_I"] = self.pars["N"] - self.pars["N_E"] # number of inhibitory neurons + self.pars["K_E"] = int(self.pars["beta"] * self.pars["K"]) # number of excitatory inputs per neuron + self.pars["K_I"] = self.pars["K"] - self.pars["K_E"] # number of inhibitory inputs per neuron + + # conversion of PSP amplitudes to PSC amplitudes + self.pars["J_unit"] = unit_psp_amplitude( + self.pars["tau_m"], self.pars["C_m"], self.pars["tau_s"] + ) # unit PSP amplitude + self.pars["I_E"] = self.pars["J_E"] / self.pars["J_unit"] # EPSC amplitude for local inputs (pA) + self.pars["I_I"] = -self.pars["g"] * self.pars["I_E"] # IPSC amplitude (pA) + self.pars["I_X"] = self.pars["I_E"] # EPSC amplitude for external inputs (pA) + + # rate of external Poissonian sources + self.pars["nu_theta"] = ( + 1000.0 + * self.pars["theta"] + * self.pars["C_m"] + / (self.pars["I_X"] * np.exp(1.0) * self.pars["tau_m"] * self.pars["tau_s"]) + ) + self.pars["nu_X"] = self.pars["eta"] * self.pars["nu_theta"] + + # number of neurons spikes are recorded from + if self.pars["N_rec_spikes"] == "all": + self.pars["N_rec_spikes"] = self.pars["N"] + + return + + ############################################## + def create(self): + """ + Create and configure all network nodes (neurons + recording and stimulus devices), + incl. setting of initial conditions. + + A dictionary containing all node IDs is stored as model attribute self.nodes. + + """ + print("\nCreating and configuring nodes...") + + # create neuron populations + pop_all = nest.Create(self.pars["neuron_model"], self.pars["N"], self.__neuron_params) # overall population + + if self.pars["neuron_model"] == "iaf_psc_alpha": + # pop_all = nest.Create("iaf_psc_alpha", self.pars["N"], self.__neuron_params) # overall population + # set random initial membrane potentials + random_vm = nest.random.uniform(self.pars["V_init_min"], self.pars["V_init_max"]) + nest.GetLocalNodeCollection(pop_all).V_m = random_vm + elif self.pars["neuron_model"] == "ignore_and_fire": + # pop_all = nest.Create("ignore_and_fire", self.pars["N"]) # overall population + # pop_all.rate = np.random.uniform(low=self.pars["ignore_and_fire_pars"]["rate_dist"][0],high=self.pars["ignore_and_fire_pars"]["rate_dist"][1],size=self.pars["N"]) + # pop_all.phase = np.random.uniform(low=self.pars["ignore_and_fire_pars"]["phase_dist"][0],high=self.pars["ignore_and_fire_pars"]["phase_dist"][1],size=self.pars["N"]) + + ## better, but not working yet: + pop_all.rate = nest.random.uniform( + min=self.pars["ignore_and_fire_pars"]["rate_dist"][0], + max=self.pars["ignore_and_fire_pars"]["rate_dist"][1], + ) + pop_all.phase = nest.random.uniform( + min=self.pars["ignore_and_fire_pars"]["phase_dist"][0], + max=self.pars["ignore_and_fire_pars"]["phase_dist"][1], + ) + + pop_E = pop_all[: self.pars["N_E"]] # population of exitatory neurons + pop_I = pop_all[self.pars["N_E"] :] # population of inhibitory neurons + + # create external Poissonian sources (stimulus) + poisson = nest.Create("poisson_generator", params={"rate": self.pars["nu_X"]}) + + # create recording devices + if self.pars["record_spikes"]: + # create, configure and connect spike detectors + spike_recorder = nest.Create("spike_recorder", {"record_to": "ascii", "label": "spikes"}) + else: + spike_recorder = None + + # configure connections + nest.CopyModel( + "stdp_pl_synapse_hom_hpc", + "excitatory_plastic", + { + "weight": self.pars["I_E"], + "delay": self.pars["delay"], + "alpha": self.pars["stdp_alpha"], + "lambda": self.pars["stdp_lambda"], + "mu": self.pars["stdp_mu_plus"], + "tau_plus": self.pars["stdp_tau_plus"], + }, + ) + + # if self.pars["record_weights"]: + # nest.SetDefaults("excitatory_plastic",{"weight_recorder": weight_recorder}) + + nest.CopyModel( + "static_synapse_hpc", "excitatory_static", {"weight": self.pars["I_E"], "delay": self.pars["delay"]} + ) + nest.CopyModel("static_synapse_hpc", "inhibitory", {"weight": self.pars["I_I"], "delay": self.pars["delay"]}) + nest.CopyModel("static_synapse_hpc", "external", {"weight": self.pars["I_X"], "delay": self.pars["delay"]}) + + # store nodes in model instance + self.nodes = {} + self.nodes["pop_all"] = pop_all + self.nodes["pop_E"] = pop_E + self.nodes["pop_I"] = pop_I + self.nodes["poisson"] = poisson + self.nodes["spike_recorder"] = spike_recorder + # self.nodes["weight_recorder"] = weight_recorder + + return + + ############################################## + def connect(self): + """ + Connect network and devices. + + """ + + print("\nConnecting network and devices...") + ## fetch neuron populations and device ids + pop_all = self.nodes["pop_all"] + pop_E = self.nodes["pop_E"] + pop_I = self.nodes["pop_I"] + poisson = self.nodes["poisson"] + spike_recorder = self.nodes["spike_recorder"] + + # connect network + ## EE connections (plastic) + nest.Connect( + pop_E, + pop_E, + conn_spec={ + "rule": "fixed_indegree", + "indegree": self.pars["K_E"], + "allow_autapses": self.pars["allow_autapses"], + "allow_multapses": self.pars["allow_multapses"], + }, + syn_spec="excitatory_plastic", + ) + + ## EI connections (static) + nest.Connect( + pop_E, + pop_I, + conn_spec={ + "rule": "fixed_indegree", + "indegree": self.pars["K_E"], + "allow_autapses": self.pars["allow_autapses"], + "allow_multapses": self.pars["allow_multapses"], + }, + syn_spec="excitatory_static", + ) + + ## IE and II connections (static) + nest.Connect( + pop_I, + pop_all, + conn_spec={ + "rule": "fixed_indegree", + "indegree": self.pars["K_I"], + "allow_autapses": self.pars["allow_autapses"], + "allow_multapses": self.pars["allow_multapses"], + }, + syn_spec="inhibitory", + ) + + # connect external Poissonian sources (stimulus) + nest.Connect(poisson, pop_all, syn_spec="external") + + # connect recording devices (to the first N_rec_spikes neurons) + if self.pars["record_spikes"]: + nest.Connect(pop_all[: self.pars["N_rec_spikes"]], spike_recorder) + + """ + Since the introduction of the 5g kernel in NEST 2.16.0 + the full connection infrastructure, including presynaptic connectivity, + is set up in the preparation phase of the simulation. + The preparation phase is usually induced by the first + "nest.Simulate()" call. For including this phase in measurements of the + connection time, we induce it here explicitly by calling ``nest.Prepare()``. + """ + nest.Prepare() + nest.Cleanup() + + return + + ############################################## + def simulate(self, t_sim): + """ + Run simulation. + + Arguments + --------- + t_sim: float + Simulation time (ms). + + """ + + print("\nSimulating...") + + nest.Simulate(t_sim) + + return + + ############################################## + def save_parameters(self, filename_root, path): + """ + Save model-instance parameters to file. + + Arguments + --------- + filename_root: str + Root of file name. + + path: str + File path + + """ + + import json + + json.dump(self.pars, open("%s/%s.json" % (path, filename_root), "w"), indent=4) + + return + + ############################################## + def get_connectivity(self, pop_pre, pop_post, filename=None): + """ + Extract connectivity for subpopulations pop_pre and pop_post + and store in file filename (unless filename=None [default]). + + Arguments + --------- + pop_pre: NodeCollection + Presynaptic neuron population. + + pop_post: NodeCollection + Postsynaptic neuron population. + + filename: str + Name of file to store connectivity data. If filename ends in ".gz", + the file will be compressed in gzip format. Set filename=None (default) + to prevent storage on disk. + + Returns + ------- + C: numpy.ndarray + Lx4 array containing connectivity information: + + C[:,0]: source id + C[:,1]: target id + C[:,2]: synaptic weight + C[:,3]: delay (ms) + + (L = len(pop_pre)*len(pop_post) = number of connections. + + """ + print("Extracting connectivity...") + + conns = nest.GetConnections(source=pop_pre, target=pop_post) + + C = np.zeros((len(conns), 4)) + C[:, 0] = conns.get()["source"] + C[:, 1] = conns.get()["target"] + C[:, 2] = conns.get()["weight"] + C[:, 3] = conns.get()["delay"] + + if filename: + np.savetxt(filename, C, fmt="%d\t%d\t%.3e\t%.3e", header=" source \t target \t weight \tdelay (ms)") + + return C + + +############################################## +def get_default_parameters(): + """ + Import default model-parameter file. + + Returns + ------- + pars: dict + Parameter dictionary. + + """ + + import parameter_dicts + + pars = parameter_dicts.pars + + return pars + + +############################################## +def get_data_file_list(path, label): + """ + Searches for files with extension "*.dat" in directory "path" with names starting with "label", + and returns list of file names. + + Arguments + --------- + path: str + Path containing spike files. + + label: str + Spike file label (file name root). + + Returns + ------- + files: list(str) + List of file names + + + """ + + ## get list of files names + files = [] + for file_name in os.listdir(path): + if file_name.endswith(".dat") and file_name.startswith(label): + files += [file_name] + files.sort() + + assert len(files) > 0, "No files of type '%s*.dat' found in path '%s'." % (label, path) + + return files + + +############################################## +# def load_spike_data(path, label, skip_rows = 3): +def load_spike_data(path, label, time_interval=None, pop=None, skip_rows=3): + """ + Load spike data from files. + + Arguments + --------- + path: str + Path containing spike files. + + label: str + Spike file label (file name root). + + time_interval: None (default) or tuple (optional) + Start and stop of observation interval (ms). All spikes outside this interva are discarded. + If None, all recorded spikes are loaded. + + pop: None (default) or nest.NodeCollection (optional) + Oberserved neuron population. All spike sendes that are not part of this population are discarded. + If None, all recorded spikes are loaded. + + skip_rows: int (optional) + Number of rows to be skipped while reading spike files (to remove file headers). The default is 3. + + Returns + ------- + spikes: numpy.ndarray + Lx2 array of spike senders spikes[:,0] and spike times spikes[:,1] (L = number of spikes). + + """ + + if type(time_interval) == tuple: + print("Loading spike data in interval (%.1f ms, %.1f ms] ..." % (time_interval[0], time_interval[1])) + else: + print("Loading spike data...") + + files = get_data_file_list(path, label) + + ## open spike files and read data + spikes = [] + for file_name in files: + try: + spikes += [ + np.loadtxt("%s/%s" % (path, file_name), skiprows=skip_rows) + ] ## load spike file while skipping the header + except: + print("Error: %s" % sys.exc_info()[1]) + print( + "Remove non-numeric entries from file %s (e.g. in file header) \ + by specifying (optional) parameter 'skip_rows'.\n" + % (file_name) + ) + + spikes = np.concatenate(spikes) + + ## extract spikes in specified time interval + if time_interval != None: + if type(time_interval) == tuple: + ind = (spikes[:, 1] >= time_interval[0]) * (spikes[:, 1] <= time_interval[1]) + spikes = spikes[ind, :] + else: + print("Warning: time_interval must be a tuple or None. All spikes are loaded.") + + if type(pop) == nest.NodeCollection: + spikes_subset = [] + for cn, nid in enumerate(pop): ## loop over all neurons + print( + "Spike extraction from %d/%d (%d%%) neurons completed" + % (cn + 1, len(pop), 1.0 * (cn + 1) / len(pop) * 100), + end="\r", + ) + ind = np.where(spikes[:, 0] == nid)[0] + spikes_subset += list(spikes[ind, :]) + spikes = np.array(spikes_subset) + elif pop == None: + pass + else: + print("Warning: pop must be a NEST NodeCollection or None. All spikes are loaded.") + print() + + return spikes + + +############################################## +def load_connectivity_data(path, label, skip_rows=1): + """ + Load connectivity data (weights and delays) from files. + + Arguments + --------- + path: str + Path containing connectivity files. + + label: str + Connectivity file label (file name root). + + skip_rows: int, optional + Number of rows to be skipped while reading connectivity files (to remove file headers). The default is 1. + + Returns + ------- + C: numpy.ndarray + Lx4 array containing connectivity information: + + C[:,0]: source id + C[:,1]: target id + C[:,2]: synaptic weight + C[:,3]: delay (ms) + + (L = len(pop_pre)*len(pop_post) = number of connections. + + """ + + files = get_data_file_list(path, label) + + ## open weight files and read data + C = [] + for file_name in files: + try: + C += [np.loadtxt("%s/%s" % (path, file_name), skiprows=skip_rows)] ## load file while skipping the header + except: + print("Error: %s" % sys.exc_info()[1]) + print( + "Remove non-numeric entries from file %s (e.g. in file header) by specifying (optional) parameter 'skip_rows'.\n" + % (file_name) + ) + + C = np.concatenate(C) + + return C + + +############################################# + + +def unit_psp_amplitude(tau_m, C_m, tau_s): + """ + Compute PSP maximum (mV) for LIF with alpha-shaped PSCs with unit amplitude 1. + + Arguments + --------- + tau_m: float + Membrane time constant (ms). + + C_m: float + Membrane capacitance (pF). + + tau_s: float + Synaptic time constant (ms). + + + Returns + ------- + J_unit: float + Unit-PSP amplitude (mV). + + """ + + a = tau_s / tau_m + b = 1.0 / tau_s - 1.0 / tau_m + + # time of PSP maximum + t_max = 1.0 / b * (-LambertWm1(-a * np.exp(a)) - a) + + J_unit = ( + np.exp(1.0) + / C_m + / (1.0 - tau_s / tau_m) + * ((np.exp(-t_max / tau_m) - np.exp(-t_max / tau_s)) / b - t_max * np.exp(-t_max / tau_s)) + ) + + return J_unit + + +############################################## +def LambertWm1(x): + y = scipy.special.lambertw(x, k=-1 if x < 0 else 0).real + + return y + + +############################################## +def get_index(x, y): + """ + Return indices of x where x==y. + + Arguments + --------- + x: list or numpy.ndarray of int, float, str + + y: int, float, str + + Returns + ------- + ind: numpy.ndarray + Index array + + """ + return np.where(x == y)[0] + + +############################################## +def get_connectivity_matrix(connectivity, pop_pre=[], pop_post=[]): + """ + Generate connectivity matrix from connectivity data in "connectivity" + for the (sub-)set of source and target neurons in "pop_pre" and "pop_post". + If "pop_pre" or "pop_post" are empty (default), the arrays of source and + target neurons will be constructed from "connectivity". + + Arguments + --------- + connectivity: numpy.ndarray + Lx4 array containing connectivity information: + + connectivity[:,0]: source id + connectivity[:,1]: target id + connectivity[:,2]: synaptic weight + connectivity[:,3]: delay (ms) + + (L = len(pop_pre)*len(pop_post) = number of connections + + pop_pre: numpy.ndarray + Array of source ids (default: []) + + pop_post: numpy.ndarray + Array of target ids (default: []) + + Returns + ------- + W: numpy.ndarray + Connectivity matrix of shape LTxLS, with number of targets LT and number of sources LS + + """ + + print("\nGenerating connectivity matrix...") + + if len(pop_pre) == 0: + pop_pre = np.unique(connectivity[:, 0]) + if len(pop_post) == 0: + pop_post = np.unique(connectivity[:, 1]) + + ## initialise weight matrix + W = np.zeros([len(pop_post), len(pop_pre)]) ## convention: pre = columns, post = rows + + ## fill weight matrix + for c in range(connectivity.shape[0]): + W[get_index(pop_post, connectivity[c, 1]), get_index(pop_pre, connectivity[c, 0])] = connectivity[c, 2] + + return W, pop_pre, pop_post + + +############################################## +def get_weight_distribution(connectivity, weights): + return np.histogram(connectivity[:, 2], weights, density=True)[0] + + +############################################## diff --git a/pynest/examples/ignore_and_fire/parameter_dicts.py b/pynest/examples/ignore_and_fire/parameter_dicts.py new file mode 100644 index 0000000000..3f311ec34d --- /dev/null +++ b/pynest/examples/ignore_and_fire/parameter_dicts.py @@ -0,0 +1,79 @@ +""" +Parameter dictionary +==================== + + +""" + +## default parameters for TwoPopulationNetworkPlastic + +pars = {} + +pars["model_name"] = "TwoPopulationNetworkPlastic" # model name + +# network and connectivity parameters +pars["N"] = 12500 # total number of neurons +pars["K"] = 1250 # total number of inputs per neuron from local network +pars["beta"] = 0.8 # fraction of excitatory neurons/inputs + +pars["allow_autapses"] = False +pars["allow_multapses"] = True + +# neuron parameters + +# pars["neuron_model"] = "iaf_psc_alpha" +pars["neuron_model"] = "ignore_and_fire" + +pars["E_L"] = 0.0 # resting membrane potential(mV) +pars["C_m"] = 250.0 # membrane capacity (pF) +pars["tau_m"] = 20.0 # membrane time constant (ms) +pars["t_ref"] = 2.0 # duration of refractory period (ms) +pars["theta"] = 20.0 # spike threshold(mV) +pars["V_reset"] = 0.0 # reset potential(mV) + +## needed for ignore_and_fire version of the model +pars["ignore_and_fire_pars"] = {} +pars["ignore_and_fire_pars"]["rate_dist"] = [0.5, 1.5] +pars["ignore_and_fire_pars"]["phase_dist"] = [0.01, 1.0] + +# stimulus parameters +pars["I_DC"] = 0.0 # (constant) external input current (pA) +pars["eta"] = 1.2 # rate of external Poissonian sources relative to threshold rate + +# synapse parameters +pars["J_E"] = 0.5 # EPSP amplitude (mV) +pars["g"] = 10.0 # relative IPSP amplitude (JI=-g*JE) +pars["delay"] = 1.5 # spike transmission delay (ms) +pars["tau_s"] = 2.0 # synaptic time constant (ms) + +pars["stdp_alpha"] = 0.1 # relative magnitude of weight update for acausal firing +pars["stdp_lambda"] = 20.0 # magnitude of weight update for causal firing +pars["stdp_mu_plus"] = 0.4 # weight dependence exponent for causal firing +pars["stdp_tau_plus"] = 15.0 # time constant of weight update for causal firing (ms) +pars["stdp_tau_minus"] = 30.0 # time constant of weight update for acausal firing (ms) +pars["stdp_w_0"] = 1.0 # reference weight (pA) + +# initial conditions +pars["V_init_min"] = pars["E_L"] # min of initial membrane potential (mV) +pars["V_init_max"] = pars["theta"] # min of initial membrane potential (mV) + +# data recording +pars["record_spikes"] = False # if True: set up spike detectors and record spikes +pars["N_rec_spikes"] = "all" # number of neurons to record spikes from; if "all", spikes from all neurons are recorded + +# pars["record_weights"] = False # True: record weights of plastic synapses +# pars["weight_recording_start_time"] = 0. # start time of weight recording (ms) + +# simulation parameters +pars["T"] = 10000.0 # simulation time +pars["dt"] = 2**-3 # simulation resolution (ms) !!! revise documentation (incl delay) +pars["tics_per_step"] = 2**7 # number of tics per time step (defines resolution of time variables in NEST) +pars["seed"] = 1 # seed for random number generator +pars["n_threads"] = 4 # number of threads for simulation +# (note: varying the number of threads leads to different random-number sequences, +# and, hence, to different results) + +pars["print_simulation_progress"] = True # print network time and realtime factor +pars["nest_verbosity"] = "M_WARNING" # "M_FATAL", "M_ERROR", "M_WARNING", "M_DEPRECATED", "M_INFO", "M_ALL" + +pars["data_path"] = "./" + pars["neuron_model"] diff --git a/pynest/examples/ignore_and_fire/reference_data/README.rst b/pynest/examples/ignore_and_fire/reference_data/README.rst new file mode 100644 index 0000000000..f39459174a --- /dev/null +++ b/pynest/examples/ignore_and_fire/reference_data/README.rst @@ -0,0 +1,2 @@ +readme +====== diff --git a/pynest/examples/ignore_and_fire/scaling.png b/pynest/examples/ignore_and_fire/scaling.png new file mode 100644 index 0000000000000000000000000000000000000000..0963ca54a44c81a9aa28258d40e39e4e8e0245cb GIT binary patch literal 91904 zcmb@ucRbg9|29q`$)-qUC99G$lO0jn6;2sZNk(Sblu<^Kv!Sv{l8`e(R#uv3Quatl zNM`1Jyg0A#_x?S8_x<>N|M+=yUDtV?`F!5%IgaOX9M5;Co{k0s9Ty!11qH)lO*I1w zinT!$6jaY>Y4C4S)&w!)fA+hn8@nBIw03j1bU90*ZRzG@@91WK-ir6qSr^yyju#}v zq{Q~_;Bn0s&o`X+%+LctrgF1)oo~GSlA){3Ca`UbymCx3wdghJp-}FomN6#3x2#_8 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zm@Gw}g@SO6Q<1^8tI;^I!_jL-9)WqYvVjD2{gKQmr%<$7uCpHJOK@12DF54A={@87 l-{A7V|Bj&l|NmEWJX2ZQ3_TAma-qZ5A!RM4M~W6#{sR?LzAXR% literal 0 HcmV?d00001 diff --git a/pynest/examples/ignore_and_fire/scaling.py b/pynest/examples/ignore_and_fire/scaling.py new file mode 100644 index 0000000000..b81037163e --- /dev/null +++ b/pynest/examples/ignore_and_fire/scaling.py @@ -0,0 +1,309 @@ +""" +Example scaling +=============== + +* demonstrate scaling experiments with the Two population STDP network testcase, +* illustrate the problems arising with the standard ``integrate-and-fire`` dynamics, and +* describe how to perform exact scaling experiments by using ``ignore_and_fire`` neurons. +""" + +import sys +import time + +sys.path.append(r"../") +import json + +import matplotlib.pyplot as plt +import model +import numpy +from matplotlib import gridspec + +########################################################## +## parameters +neuron_models = ["iaf_psc_alpha", "ignore_and_fire"] +# neuron_models = ["iaf_psc_alpha"] +# neuron_models = ["ignore_and_fire"] +Ns = numpy.arange(1250, 15000, 1250) +# Ns = numpy.arange(13750,15000,1250) + +data_path_root = "./data" + +simulate = True +# simulate = False +analyse = True +# analyse = False + + +########################################################## +## network construction and simulation +def run_model(neuron_model, N, data_path_root): + """ + Builds an instance of the model for a given network size and neuron model, + simulates it, records spiking activity and synaptic weight data, measures + model construction and simulation times, and stores this data in a json file. + """ + + parameters = model.get_default_parameters() + + ## overwrite default parameters + parameters["record_spikes"] = True + parameters["neuron_model"] = neuron_model ## choose model flavor + parameters["N"] = N ## specify scale (network sizes) + parameters["data_path"] = data_path_root + "_" + neuron_model + "_" + str(N) + + ## create and simulate network + start = time.time() + model_instance = model.Model(parameters) + model_instance.create() + model_instance.connect() + stop = time.time() + build_time = stop - start + + start = time.time() + model_instance.simulate(model_instance.pars["T"]) + stop = time.time() + sim_time = stop - start + + ## store model instance parameters + model_instance.save_parameters("model_instance_parameters", model_instance.pars["data_path"]) + + ## record connectivity at end of simulation + subset_size = 200 ## number of pre- and post-synaptic neurons weights are extracted from + pop_pre = model_instance.nodes["pop_E"][:subset_size] + pop_post = model_instance.nodes["pop_E"][:subset_size] + C = model_instance.get_connectivity( + pop_pre, pop_post, model_instance.pars["data_path"] + "/" + "connectivity_postsim.dat" + ) + + ## data analysis + ### time and population averaged firing rate + spikes = model.load_spike_data( + model_instance.pars["data_path"], "spikes-%d" % (numpy.array(model_instance.nodes["spike_recorder"])[0]) + ) + rate = time_and_population_averaged_spike_rate( + spikes, (0.0, model_instance.pars["T"]), model_instance.pars["N_rec_spikes"] + ) + + ### synaptic weight statistics after simulation + connectivity_postsim = model.load_connectivity_data(model_instance.pars["data_path"], "connectivity_postsim") + weight_stats = data_statistics(connectivity_postsim[:, 2]) + # weights = numpy.arange(0.,150.1,0.5) + # whist_postsim = model.get_weight_distribution(connectivity_postsim,weights) + # print(whist_postsim) + + ############## + print() + print("model: %s" % model_instance.pars["neuron_model"]) + print("\tN = %d" % model_instance.pars["N"]) + print("\t\taverage firing rate: nu=%.2f/s" % (rate)) + print( + "\t\tweight distribution [min,mean,median,sd,max] = [%.1f, %.1f, %.1f, %.1f, %.1f] pA" + % (weight_stats["min"], weight_stats["mean"], weight_stats["median"], weight_stats["sd"], weight_stats["max"]) + ) + print() + ############## + + data = {} + data["build_time"] = build_time + data["sim_time"] = sim_time + data["rate"] = rate + data["weight_mean"] = weight_stats["mean"] + data["weight_sd"] = weight_stats["sd"] + data["weight_min"] = weight_stats["min"] + data["weight_max"] = weight_stats["max"] + data["weight_median"] = weight_stats["median"] + + save_dict_as_json(data, "data", model_instance.pars["data_path"]) + + return data + + +########################################################## + + +def time_and_population_averaged_spike_rate(spikes, time_interval, pop_size): + """ + Calculates the time and population averaged firing rate for an ensemble + of spike trains within a specified time interval. + """ + D = time_interval[1] - time_interval[0] + n_events = sum((spikes[:, 1] >= time_interval[0]) * (spikes[:, 1] <= time_interval[1])) + rate = n_events / D * 1000.0 / pop_size + return rate + + +########################################################## +def data_statistics(data): + """ + Calculates and collet simple statistics for a list of data samples. + """ + stats = {} + stats["mean"] = numpy.nanmean(data) + stats["sd"] = numpy.nanstd(data) + stats["min"] = numpy.nanmin(data) + stats["max"] = numpy.nanmax(data) + stats["median"] = numpy.nanmedian(data) + return stats + + +############################################## +def save_dict_as_json(data_dict, filename_root, path): + """ + Save python dictionary as json file. + + Arguments + --------- + data_dict: dict + Data dictionary. + + filename_root: str + Root of file name. + + path: str + File path + + """ + + import json + + json.dump(data_dict, open("%s/%s.json" % (path, filename_root), "w")) + + return + + +########################################################## +def load_data(neuron_model, N, data_path_root): + """ + Loads a data dictionary from a json file. + """ + data = {} + + ## read data from json file + data_path = data_path_root + "_" + neuron_model + "_" + str(N) + with open(data_path + "/data.json") as f: + data = json.load(f) + + return data + + +########################################################## + +if analyse: + build_time = numpy.zeros((len(neuron_models), len(Ns))) + sim_time = numpy.zeros_like(build_time) + rate = numpy.zeros_like(build_time) + weight_mean = numpy.zeros_like(build_time) + weight_sd = numpy.zeros_like(build_time) + weight_min = numpy.zeros_like(build_time) + weight_max = numpy.zeros_like(build_time) + weight_median = numpy.zeros_like(build_time) + +for cm, neuron_model in enumerate(neuron_models): + print("%s\n" % neuron_model) + + for cN, N in enumerate(Ns): + print("\tN = %d" % N) + + if simulate: + data = run_model(neuron_model, int(N), data_path_root) + + if analyse: + data = load_data(neuron_model, int(N), data_path_root) + + build_time[cm, cN] = data["build_time"] + sim_time[cm, cN] = data["sim_time"] + rate[cm, cN] = data["rate"] + weight_mean[cm, cN] = data["weight_mean"] + weight_sd[cm, cN] = data["weight_sd"] + weight_min[cm, cN] = data["weight_min"] + weight_max[cm, cN] = data["weight_max"] + weight_median[cm, cN] = data["weight_median"] + + print() + print() + +if analyse: + ## plotting + fig = plt.figure(num=3, figsize=(3, 4), dpi=300) + plt.clf() + + plt.rcParams.update( + { + "text.usetex": True, + "font.size": 8, + "axes.titlesize": 8, + "legend.fontsize": 8, + } + ) + + gs = gridspec.GridSpec(3, 1) + + ## build and sim times + ax1 = fig.add_subplot(gs[0, 0]) + + ## firing rate + ax2 = fig.add_subplot(gs[1, 0]) + + ## weight stat + ax3 = fig.add_subplot(gs[2, 0]) + + ms = 4 + lw = 2 + clrs = ["0", "0.8"] + + for cm, neuron_model in enumerate(neuron_models): + ## sim time + ax1.plot( + Ns, + sim_time[cm, :], + "-o", + mfc=clrs[cm], + mec=clrs[cm], + ms=ms, + lw=lw, + color=clrs[cm], + label=r"%s" % neuron_model, + ) + ax1.set_xlim(Ns[0], Ns[-1]) + ax1.set_xticklabels([]) + ax1.set_ylabel(r"simulation time (s)") + ax1.set_title(r"fixed in-degree $K=1250$") + + ## firing rate + ax2.plot( + Ns, rate[cm, :], "-o", mfc=clrs[cm], mec=clrs[cm], ms=ms, lw=lw, color=clrs[cm], label=r"%s" % neuron_model + ) + ax2.set_xlim(Ns[0], Ns[-1]) + ax2.set_xticklabels([]) + ax2.set_ylim(0.5, 2) + ax2.set_ylabel(r"firing rate (1/s)") + ax2.legend(loc=1) + + ## weight stat + if cm == 0: + lbl1 = "mean" + lbl2 = "mean + SD" + else: + lbl1 = "" + lbl2 = "" + + ax3.plot(Ns, weight_mean[cm, :], "-o", mfc=clrs[cm], mec=clrs[cm], lw=lw, color=clrs[cm], ms=ms, label=lbl1) + ax3.plot( + Ns, + weight_mean[cm, :] + weight_sd[cm, :], + "--", + mfc=clrs[cm], + mec=clrs[cm], + lw=lw, + color=clrs[cm], + ms=ms, + label=lbl2, + ) + ax3.set_xlim(Ns[0], Ns[-1]) + ax3.set_ylim(10, 100) + ax3.set_xlabel(r"network size $N$") + ax3.set_ylabel(r"synaptic weight (pA)") + ax3.legend(loc=1) + + plt.subplots_adjust(left=0.17, bottom=0.1, right=0.95, top=0.95, hspace=0.1) + plt.savefig("scaling.png") diff --git a/pynest/examples/ignore_and_fire/scaling/README.rst b/pynest/examples/ignore_and_fire/scaling/README.rst new file mode 100644 index 0000000000..f39459174a --- /dev/null +++ b/pynest/examples/ignore_and_fire/scaling/README.rst @@ -0,0 +1,2 @@ +readme +====== diff --git a/pynest/examples/ignore_and_fire/simulation_details.rst b/pynest/examples/ignore_and_fire/simulation_details.rst new file mode 100644 index 0000000000..7abe412c37 --- /dev/null +++ b/pynest/examples/ignore_and_fire/simulation_details.rst @@ -0,0 +1,55 @@ +Two population STDP network model simulation +============================================ + +Simulation details +------------------ + +By default, this implementation is based on the +:doc:`iaf_psc_alpha ` +neuron and the +:doc:`stdp_pl_synapse_hom ` +synapse models provided in NEST. With +``pars['neuron_model']='ignore_and_fire'``, the model is configured into +a truly scalable mode where the ``integrate-and-fire`` dynamics are replaced +by an ``ignore_and_fire`` dynamics, while the plasticity dynamics +remains intact. + +The network is connected according to the +``fixed_indegree`` :ref:`connection rule ` in NEST. + +The neuron dynamics is propagated in time using exact integration +based on Rotter and Diesmann [1]_ with a simulation step +size :math:`\Delta{}t`. The synapse dynamics is updated in an +event-based manner as described by Morrison et +al. [2]_. + + + .. The model implementation runs with `NEST 3.6 `__ and `NESTML 6.0.0 `__. + +Simulation parameters +--------------------- + ++-----------------------+---------------------------------+-----------------------------+ +| Name | Value | Description | ++=======================+=================================+=============================+ +| :math:`T` | :math:`1000\,\text{ms}` | total simulation time | ++-----------------------+---------------------------------+-----------------------------+ +| :math:`\Delta{}t` | :math:`2^{-3}=0.125\,\text{ms}` | duration pof simulation step| ++-----------------------+---------------------------------+-----------------------------+ +| ``tics_per_step`` | :math:`2^7=128` | number of tics per | +| | | simulation step | +| | | :math:`\Delta{t}` | +| | | (time resolution) | ++-----------------------+---------------------------------+-----------------------------+ + +References +---------- + +.. [1] Rotter & Diesmann (1999). Exact digital simulation of time-invariant + linear systems with applications to neuronal modeling. Biological + Cybernetics 81(5-6):381-402. + doi:10.1007/s004220050570 https://doi.org/10.1007/s004220050570 + +.. [2] Morrison et al. (2007). Spike-timing dependent plasticity in + balanced random networks. Neural Computation + 19:1437-1467 10.1162/neco.2007.19.6.1437 From ecf83dbcd97f86d640316f488e65a5d8b5f86d5e Mon Sep 17 00:00:00 2001 From: Jessica Mitchell Date: Wed, 17 Jan 2024 12:32:53 +0100 Subject: [PATCH 03/38] reword text --- doc/htmldoc/examples/index.rst | 4 ++-- pynest/examples/ignore_and_fire/README.rst | 4 ++-- pynest/examples/ignore_and_fire/model.py | 6 +++--- 3 files changed, 7 insertions(+), 7 deletions(-) diff --git a/doc/htmldoc/examples/index.rst b/doc/htmldoc/examples/index.rst index c87d8d03c0..baf27cf91f 100644 --- a/doc/htmldoc/examples/index.rst +++ b/doc/htmldoc/examples/index.rst @@ -232,11 +232,11 @@ PyNEST examples .. grid:: 1 1 2 3 - .. grid-item-card:: HPC benchmark + .. grid-item-card:: Performance testing :img-top: ../static/img/nest_logo-faded.png * :doc:`../auto_examples/hpc_benchmark` - + * :doc:`../auto_examples/ignore_and_fire/index` .. grid-item-card:: Connection set algebra :img-top: ../static/img/nest_logo-faded.png diff --git a/pynest/examples/ignore_and_fire/README.rst b/pynest/examples/ignore_and_fire/README.rst index fedd941d66..799e20dfa5 100644 --- a/pynest/examples/ignore_and_fire/README.rst +++ b/pynest/examples/ignore_and_fire/README.rst @@ -1,5 +1,5 @@ -Example applications of the ``ignore_and_fire`` neuron with a two population STDP network model -=============================================================================================== +Using ``ignore_and_fire`` neuron for scaling experiments +======================================================== File structure --------------- diff --git a/pynest/examples/ignore_and_fire/model.py b/pynest/examples/ignore_and_fire/model.py index 0b7928bb14..5631aed5b5 100644 --- a/pynest/examples/ignore_and_fire/model.py +++ b/pynest/examples/ignore_and_fire/model.py @@ -1,6 +1,6 @@ """ -Two population STDP network model ---------------------------------- +Scalable two population STDP network model +------------------------------------------ In this example, we employ a simple network model describing the dynamics of a local cortical circuit at the spatial scale of ~1mm within @@ -14,7 +14,7 @@ The model used here can be configured into a truly scalable mode by -replacing the integrate-and-fire neurons by an ``ignore_and_fire`` +replacing the ``integrate-and-fire`` neurons by an ``ignore_and_fire`` dynamics. By doing so, the spike generation dynamics is decoupled from the input integration and the plasticity dynamics; the overall network activity, From ec4bdf9bf928800ed50530faeec4da43aabde5e4 Mon Sep 17 00:00:00 2001 From: Jessica Mitchell Date: Thu, 18 Jan 2024 12:49:19 +0100 Subject: [PATCH 04/38] update table formats --- doc/htmldoc/static/css/custom.css | 22 +- ...escription_TwoPopulationNetworkPlastic.rst | 355 +++++++++--------- 2 files changed, 195 insertions(+), 182 deletions(-) diff --git a/doc/htmldoc/static/css/custom.css b/doc/htmldoc/static/css/custom.css index b795d19844..cb5e63f636 100644 --- a/doc/htmldoc/static/css/custom.css +++ b/doc/htmldoc/static/css/custom.css @@ -1,4 +1,4 @@ -/* +/* * Customize css to define NEST specific colors, style and format * primary styles, for custom index.html and layout.html pages (see templates/ folder) @@ -8,7 +8,7 @@ Sphinx theme: sphinx-material https://bashtage.github.io/sphinx-material/index.html - Sphinx material theme properties use 'md' in variable names + Sphinx material theme properties use 'md' in variable names Extesion for grid / cards in reStructured Text see sphinx-design https://sphinx-design.readthedocs.io/ Sphinx design properties use the sd in variable names @@ -100,6 +100,16 @@ section#kernel-attributes-nest-nestmodule dd { font-weight: bold; } +.md-typeset table:not([class]) th { + background-color: rgba(0, 0, 0, 0.0); + color: #666; +} + +.md-typeset table:not([class]) tr{ + border-bottom-color: #e3e3e3; + border-bottom-width: 1px; +} + /************************************************************* * Rules for mermaid diagrams *************************************************************/ @@ -127,7 +137,7 @@ figure.align-default { } /************************************************************************************** - Settings for overriding material design theme + Settings for overriding material design theme Setting nest colors to override sphinx material "orange" and other default colors **************************************************************************************/ @@ -142,7 +152,7 @@ figure.align-default { font-weight: 600; } .md-typeset h1[id]:before, -.md-typeset h2[id]:before, +.md-typeset h2[id]:before, .md-typeset h3[id]:before, .md-typeset h4[id]:before { margin-top: -3.4rem; @@ -201,7 +211,7 @@ div[role="main"] .md-source { div.sd-card-title.sd-font-weight-bold.sd-d-flex-row.docutils a.reference.internal img { margin-right: 2em; margin-bottom: 0.5em; - + } .md-typeset .admonition, .md-typeset .admonition.tip { @@ -230,7 +240,7 @@ div.sd-card-title.sd-font-weight-bold.sd-d-flex-row.docutils a.reference.interna } .md-typeset .admonition.warning { border-left-color: #be1717; -} +} .md-typeset .admonition.warning > .admonition-title { background-color: #be1717; } diff --git a/pynest/examples/ignore_and_fire/ModelDescription_TwoPopulationNetworkPlastic.rst b/pynest/examples/ignore_and_fire/ModelDescription_TwoPopulationNetworkPlastic.rst index 81a742cef5..873841e0cb 100644 --- a/pynest/examples/ignore_and_fire/ModelDescription_TwoPopulationNetworkPlastic.rst +++ b/pynest/examples/ignore_and_fire/ModelDescription_TwoPopulationNetworkPlastic.rst @@ -7,19 +7,19 @@ Summary of network model ------------------------ .. list-table:: + :stub-columns: 1 - - * - Populations - - excitatory population :math:`\mathcal{E}` inhibitory population :math:`\mathcal{I}`, external Poissonian spike sources :math:`\mathcal{X}` - * - Connectivity - - sparse random connectivity respecting Dale’s principle - * - Neurons - - leaky integrate-and-fire (LIF) - * - Synapses - - linear input integration with alpha-shaped postsynaptic currents (PSCs), spike-timing dependent plasticity (STDP) for connections between - excitatory neurons - * - Input - - stationary, uncorrelated Poissonian spike trains + * - **Populations** + - excitatory population :math:`\mathcal{E}` inhibitory population :math:`\mathcal{I}`, external Poissonian spike sources :math:`\mathcal{X}` + * - **Connectivity** + - sparse random connectivity respecting Dale’s principle + * - **Neurons** + - leaky integrate-and-fire (LIF) + * - **Synapses** + - linear input integration with alpha-shaped postsynaptic currents (PSCs), spike-timing dependent plasticity (STDP) for connections between + excitatory neurons + * - **Input** + - stationary, uncorrelated Poissonian spike trains Detailed desciption of network model @@ -151,189 +151,192 @@ Connectivity -Neuron: Leaky integrate-and-fire (LIF) dynamics -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +Neuron +~~~~~~ +.. list-table:: -Dynamics of membrane potential :math:`V_{i}(t)` and -spiking activity :math:`s_i(t)` of neuro :math:`i\in\left\{1,\ldots,N\right\}`: + * - **Leaky integrate-and-fire (iaf) dynamics** + - Dynamics of membrane potential :math:`V_{i}(t)` and + spiking activity :math:`s_i(t)` of neuro :math:`i\in\left\{1,\ldots,N\right\}`: -emission of :math:`k`\ th (:math:`k=1,2,\ldots`) spike of neuron -:math:`i` at time :math:`t_{i}^{k}` if + * emission of :math:`k`\ th (:math:`k=1,2,\ldots`) spike of neuron + :math:`i` at time :math:`t_{i}^{k}` if -.. math:: - V_{i}\left(t_{i}^{k}\right)\geq\theta + .. math:: + V_{i}\left(t_{i}^{k}\right)\geq\theta -with spike threshold :math:`\theta` + with spike threshold :math:`\theta` - - reset and refractoriness: + * reset and refractoriness: -.. math:: \forall{}k,\ \forall t \in \left(t_{k}^{i},\,t_{k}^{i}+\tau_\text{ref}\right]:\quad V_{i}(t)=V_\text{reset} + .. math:: \forall{}k,\ \forall t \in \left(t_{k}^{i},\,t_{k}^{i}+\tau_\text{ref}\right]:\quad V_{i}(t)=V_\text{reset} -with refractory period :math:`\tau_\text{ref}` and reset potential -:math:`V_\text{reset}` + with refractory period :math:`\tau_\text{ref}` and reset potential + :math:`V_\text{reset}` -- spike train :math:`\displaystyle s_i(t)=\sum_k \delta(t-t_i^k)` + * spike train :math:`\displaystyle s_i(t)=\sum_k \delta(t-t_i^k)` -- subthreshold dynamics of membrane potential :math:`V_{i}(t)`: + * subthreshold dynamics of membrane potential :math:`V_{i}(t)`: -.. math:: + .. math:: - \begin{aligned} - &\forall{}k,\ \forall t \notin \left[t_{i}^{k},\,t_{i}^{k}+\tau_\text{ref}\right):\\ - &\qquad\tau_\text{m}\frac{\text{d}{}V_i(t)}{\text{d}{}t} = - \Bigl[E_\text{L}-V_i(t)\Bigr]+R_\text{m}I_i(t) - \end{aligned} + \begin{aligned} + &\forall{}k,\ \forall t \notin \left[t_{i}^{k},\,t_{i}^{k}+\tau_\text{ref}\right):\\ + &\qquad\tau_\text{m}\frac{\text{d}{}V_i(t)}{\text{d}{}t} = + \Bigl[E_\text{L}-V_i(t)\Bigr]+R_\text{m}I_i(t) + \end{aligned} -with membrane time constant :math:`\tau_\text{m}`, membrane -resistance :math:`R_\text{m}`, resting potential :math:`E_\text{L}`, -and total synaptic input current :math:`I_i(t)` + with membrane time constant :math:`\tau_\text{m}`, membrane + resistance :math:`R_\text{m}`, resting potential :math:`E_\text{L}`, + and total synaptic input current :math:`I_i(t)` Synapse: transmission ~~~~~~~~~~~~~~~~~~~~~ -Current-based synapses with alpha-function shaped postsynaptic currents (PSCs) -`````````````````````````````````````````````````````````````````````````````` +.. list-table:: + + * - **Current-based synapses with alpha-function shaped postsynaptic currents (PSCs)** -Total synaptic input current of neuron :math:`i` + - Total synaptic input current of neuron :math:`i` -.. math:: I_i(t)=I_{\text{E},i}(t)+I_{\text{I},i}(t)+I_{\text{X},i}(t) + .. math:: I_i(t)=I_{\text{E},i}(t)+I_{\text{I},i}(t)+I_{\text{X},i}(t) -- excitatory, inhibitory and external synaptic input currents + * excitatory, inhibitory and external synaptic input currents -.. math:: + .. math:: - %I_{P,i}(t)=\sum_{j\in\mathcal{P}}(\text{PSC}_{ij}*s_j)(t) - %\quad\text{for}\quad - %(P,\mathcal{P})\in\{(\exc,\Epop),(\inh,\Ipop),(\ext,\Xpop)\} - %, - \begin{aligned} - I_{\text{E},i}(t)&=\sum_{j\in\mathcal{E}}\bigl(\text{PSC}_{ij}*s_j\bigr)(t-d_{ij})\\ - I_{\text{I},i}(t)&=\sum_{j\in\mathcal{I}}\bigl(\text{PSC}_{ij}*s_j\bigr)(t-d_{ij})\\ - I_{\text{X},i}(t)&=\sum_{j\in\mathcal{X}}\bigl(\text{PSC}_{ij}*s_j\bigr)(t-d_{ij}) - \end{aligned} + %I_{P,i}(t)=\sum_{j\in\mathcal{P}}(\text{PSC}_{ij}*s_j)(t) + %\quad\text{for}\quad + %(P,\mathcal{P})\in\{(\exc,\Epop),(\inh,\Ipop),(\ext,\Xpop)\} + %, + \begin{aligned} + I_{\text{E},i}(t)&=\sum_{j\in\mathcal{E}}\bigl(\text{PSC}_{ij}*s_j\bigr)(t-d_{ij})\\ + I_{\text{I},i}(t)&=\sum_{j\in\mathcal{I}}\bigl(\text{PSC}_{ij}*s_j\bigr)(t-d_{ij})\\ + I_{\text{X},i}(t)&=\sum_{j\in\mathcal{X}}\bigl(\text{PSC}_{ij}*s_j\bigr)(t-d_{ij}) + \end{aligned} -with spike trains :math:`s_j(t)` of local -(:math:`j\in\mathcal{E}\cup\mathcal{I}`) and external sources -(:math:`j\in\mathcal{X}`), spike transmission delays :math:`d_{ij}`, -and convolution operator “:math:`*`”: -:math:`\displaystyle\bigl(f*g\bigr)(t)=\int_{-\infty}^\infty\text{d}s\,f(s)g(t-s)`) + with spike trains :math:`s_j(t)` of local + (:math:`j\in\mathcal{E}\cup\mathcal{I}`) and external sources + (:math:`j\in\mathcal{X}`), spike transmission delays :math:`d_{ij}`, + and convolution operator “:math:`*`”: + :math:`\displaystyle\bigl(f*g\bigr)(t)=\int_{-\infty}^\infty\text{d}s\,f(s)g(t-s)`) -- alpha-function shaped postsynaptic currents + * alpha-function shaped postsynaptic currents -.. math:: \text{PSC}_{ij}(t)=\hat{I}_{ij}e\tau_\text{s}^{-1}te^{-t/\tau_\text{s}}\Theta(t) + .. math:: \text{PSC}_{ij}(t)=\hat{I}_{ij}e\tau_\text{s}^{-1}te^{-t/\tau_\text{s}}\Theta(t) -with synaptic time constant :math:`\tau_\text{s}` and Heaviside -function :math:`\Theta(\cdot)` + with synaptic time constant :math:`\tau_\text{s}` and Heaviside + function :math:`\Theta(\cdot)` -- postsynaptic potential triggered by a single presynaptic spike + * postsynaptic potential triggered by a single presynaptic spike -.. math:: + .. math:: - \text{PSP}_{ij}(t)= - \hat{I}_{ij}\frac{e}{\tau_\text{s}C_\text{m}} - \left(\frac{1}{\tau_\text{m}}-\frac{1}{\tau_\text{s}}\right)^{-2} - \left(\left(\frac{1}{\tau_\text{m}}-\frac{1}{\tau_\text{s}}\right) t e^{-t/\tau_\text{s}} - e^{-t/\tau_\text{s}} + e^{-t/\tau_\text{m}} \right) \Theta(t) + \text{PSP}_{ij}(t)= + \hat{I}_{ij}\frac{e}{\tau_\text{s}C_\text{m}} + \left(\frac{1}{\tau_\text{m}}-\frac{1}{\tau_\text{s}}\right)^{-2} + \left(\left(\frac{1}{\tau_\text{m}}-\frac{1}{\tau_\text{s}}\right) t e^{-t/\tau_\text{s}} - e^{-t/\tau_\text{s}} + e^{-t/\tau_\text{m}} \right) \Theta(t) -- PSC amplitude (synaptic weight) + * PSC amplitude (synaptic weight) -.. math:: + .. math:: - \hat{I}_{ij}=\text{max}_t\bigl(\text{PSC}_{ij}(t)\bigr) - =\frac{J_{ij}}{J_\text{unit}(\tau_\text{m},\tau_\text{s},C_\text{m})} + \hat{I}_{ij}=\text{max}_t\bigl(\text{PSC}_{ij}(t)\bigr) + =\frac{J_{ij}}{J_\text{unit}(\tau_\text{m},\tau_\text{s},C_\text{m})} -parameterized by PSP amplitude -:math:`J_{ij}=\text{max}_t\bigl(\text{PSP}_{ij}(t)\bigr)` + parameterized by PSP amplitude + :math:`J_{ij}=\text{max}_t\bigl(\text{PSP}_{ij}(t)\bigr)` -- with unit PSP amplitude (PSP amplitude for :math:`\hat{I}_{ij}=1`): + with unit PSP amplitude (PSP amplitude for :math:`\hat{I}_{ij}=1`): - .. math:: + .. math:: - J_\text{unit}(\tau_\text{m},\tau_\text{s},C_\text{m}) - = \frac{e}{C_\text{m}\left(1-\frac{\tau_\text{s}}{\tau_\text{m}}\right)}\left( \frac{e^{-t_\text{max}/\tau_\text{m}} - e^{-t_\text{max}/\tau_\text{s}}}{\frac{1}{\tau_\text{s}} - \frac{1}{\tau_\text{m}}} - t_\text{max}e^{-t_\text{max}/\tau_\text{s}} \right), + J_\text{unit}(\tau_\text{m},\tau_\text{s},C_\text{m}) + = \frac{e}{C_\text{m}\left(1-\frac{\tau_\text{s}}{\tau_\text{m}}\right)}\left( \frac{e^{-t_\text{max}/\tau_\text{m}} - e^{-t_\text{max}/\tau_\text{s}}}{\frac{1}{\tau_\text{s}} - \frac{1}{\tau_\text{m}}} - t_\text{max}e^{-t_\text{max}/\tau_\text{s}} \right), -- time to PSP maximum + time to PSP maximum - .. math:: + .. math:: - t_\text{max} = - \frac{1}{\frac{1}{\tau_\text{s}} - \frac{1}{\tau_\text{m}}}\left(-W_{-1}\left(\frac{-\tau_\text{s}e^{-\frac{\tau_\text{s}}{\tau_\text{m}}}}{\tau_\text{m}}\right) - \frac{\tau_\text{s}}{\tau_\text{m}}\right), + t_\text{max} = + \frac{1}{\frac{1}{\tau_\text{s}} - \frac{1}{\tau_\text{m}}}\left(-W_{-1}\left(\frac{-\tau_\text{s}e^{-\frac{\tau_\text{s}}{\tau_\text{m}}}}{\tau_\text{m}}\right) - \frac{\tau_\text{s}}{\tau_\text{m}}\right), - and Lambert-W function :math:`\displaystyle W_{-1}(x)` for - :math:`\displaystyle x \ge -1/e` + and Lambert-W function :math:`\displaystyle W_{-1}(x)` for + :math:`\displaystyle x \ge -1/e` Synapse: plasticity ~~~~~~~~~~~~~~~~~~~ -Spike-timing dependent plasticity (STDP) with power-law weight dependence and all-to-all spike pairing scheme -````````````````````````````````````````````````````````````````````````````````````````````````````````````` +.. list-table:: -See Morrison et al. [1]_ for connections between excitatory neurons. + * - **Spike-timing dependent plasticity (STDP) with power-law weight dependence and all-to-all spike pairing scheme.** + See Morrison et al. [1]_ for connections between excitatory neurons. -Dynamics of synaptic weights :math:`J_{ij}(t)` :math:`\forall{}i\in\mathcal{E}, j\in\mathcal{E}`: + - Dynamics of synaptic weights :math:`J_{ij}(t)` :math:`\forall{}i\in\mathcal{E}, j\in\mathcal{E}`: - .. math:: + .. math:: - \begin{aligned} - &\forall J_{ij}\ge{}0: \\[1ex] - &\quad - \frac{\text{d}}{}J_{ij}{\text{d}{}t}= - \lambda^+f^+(J_{ij})\sum_k x^+_j(t)\delta\Bigl(t-[t_i^k+d_{ij}]\Bigr) - + \lambda^-f^-(J_{ij})\sum_l x^-_i(t)\delta\Big(t-[t_j^l-d_{ij}]\Bigr)\\[1ex] - &\forall{}\{t|J_{ij}(t)<0\}: \quad J_{ij}(t)=0 \quad \text{(clipping)} - \end{aligned} + \begin{aligned} + &\forall J_{ij}\ge{}0: \\[1ex] + &\quad + \frac{\text{d}}{}J_{ij}{\text{d}{}t}= + \lambda^+f^+(J_{ij})\sum_k x^+_j(t)\delta\Bigl(t-[t_i^k+d_{ij}]\Bigr) + + \lambda^-f^-(J_{ij})\sum_l x^-_i(t)\delta\Big(t-[t_j^l-d_{ij}]\Bigr)\\[1ex] + &\forall{}\{t|J_{ij}(t)<0\}: \quad J_{ij}(t)=0 \quad \text{(clipping)} + \end{aligned} - with + with - - pre- and postsynaptic spike times :math:`\{t_j^l|l=1,2,\ldots\}` and - :math:`\{t_i^k|k=1,2,\ldots\}`, + - pre- and postsynaptic spike times :math:`\{t_j^l|l=1,2,\ldots\}` and + :math:`\{t_i^k|k=1,2,\ldots\}`, - - magnitude :math:`\lambda^+=\lambda` of weight update for causal - firing (postsynaptic spike following presynaptic spikes: - :math:`t_i^k>t_j^l`), + - magnitude :math:`\lambda^+=\lambda` of weight update for causal + firing (postsynaptic spike following presynaptic spikes: + :math:`t_i^k>t_j^l`), - - magnitude :math:`\lambda^-=-\alpha\lambda` of weight update for - acausal firing (presynaptic spike following postsynaptic spikes: - :math:`t_i^k Date: Fri, 19 Jan 2024 09:24:11 +0100 Subject: [PATCH 05/38] add network sketch --- ...escription_TwoPopulationNetworkPlastic.rst | 3 + ...orkSketch_TwoPopulationNetworkPlastic.fodg | 1269 +++++++++++++++++ .../ignore_and_fire/parameter_dicts.py | 4 +- 3 files changed, 1274 insertions(+), 2 deletions(-) create mode 100644 pynest/examples/ignore_and_fire/NetworkSketch_TwoPopulationNetworkPlastic.fodg diff --git a/pynest/examples/ignore_and_fire/ModelDescription_TwoPopulationNetworkPlastic.rst b/pynest/examples/ignore_and_fire/ModelDescription_TwoPopulationNetworkPlastic.rst index 873841e0cb..7d203c698d 100644 --- a/pynest/examples/ignore_and_fire/ModelDescription_TwoPopulationNetworkPlastic.rst +++ b/pynest/examples/ignore_and_fire/ModelDescription_TwoPopulationNetworkPlastic.rst @@ -21,6 +21,9 @@ Summary of network model * - **Input** - stationary, uncorrelated Poissonian spike trains +.. figure:: NetworkSketch_TwoPopulationNetworkPlastic.fodg + + For legend see https://doi.org/10.1371/journal.pcbi.1010086.g008 Detailed desciption of network model ------------------------------------ diff --git a/pynest/examples/ignore_and_fire/NetworkSketch_TwoPopulationNetworkPlastic.fodg b/pynest/examples/ignore_and_fire/NetworkSketch_TwoPopulationNetworkPlastic.fodg new file mode 100644 index 0000000000..626735d9fc 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==================== +Default parameters for Two population STDP network model (TwoPopulationNetworkPlastic) """ -## default parameters for TwoPopulationNetworkPlastic pars = {} -pars["model_name"] = "TwoPopulationNetworkPlastic" # model name +pars["model_name"] = "TwoPopulationNetworkPlastic" # Network model name # network and connectivity parameters pars["N"] = 12500 # total number of neurons From 997b209db9022b03587e0f11311920b6a8403992 Mon Sep 17 00:00:00 2001 From: Jessica Mitchell Date: Thu, 25 Jan 2024 08:58:13 +0100 Subject: [PATCH 06/38] add sketck --- doc/htmldoc/conf.py | 2 +- ...escription_TwoPopulationNetworkPlastic.rst | 14 +- ..._TwoPopulationNetworkPlastic_converted.svg | 609 ++++++++++++++++++ 3 files changed, 620 insertions(+), 5 deletions(-) create mode 100644 pynest/examples/ignore_and_fire/NetworkSketch_TwoPopulationNetworkPlastic_converted.svg diff --git a/doc/htmldoc/conf.py b/doc/htmldoc/conf.py index 1de5d7b4a8..c1b6722e1a 100644 --- a/doc/htmldoc/conf.py +++ b/doc/htmldoc/conf.py @@ -80,7 +80,7 @@ "gallery_dirs": "auto_examples", "plot_gallery": "False", "download_all_examples": False, - "copyfile_regex": r".*\.rst|.*\.png", + "copyfile_regex": r".*\.rst|.*\.png|.*\.svg", } # General information about the project. diff --git a/pynest/examples/ignore_and_fire/ModelDescription_TwoPopulationNetworkPlastic.rst b/pynest/examples/ignore_and_fire/ModelDescription_TwoPopulationNetworkPlastic.rst index 7d203c698d..5484a35603 100644 --- a/pynest/examples/ignore_and_fire/ModelDescription_TwoPopulationNetworkPlastic.rst +++ b/pynest/examples/ignore_and_fire/ModelDescription_TwoPopulationNetworkPlastic.rst @@ -21,9 +21,11 @@ Summary of network model * - **Input** - stationary, uncorrelated Poissonian spike trains -.. figure:: NetworkSketch_TwoPopulationNetworkPlastic.fodg + * - + - .. figure:: NetworkSketch_TwoPopulationNetworkPlastic_converted.svg - For legend see https://doi.org/10.1371/journal.pcbi.1010086.g008 + Network sketch. For legend see `Fig. 8 `_ + in Senk et al. [1]_. Detailed desciption of network model ------------------------------------ @@ -278,7 +280,7 @@ Synapse: plasticity .. list-table:: * - **Spike-timing dependent plasticity (STDP) with power-law weight dependence and all-to-all spike pairing scheme.** - See Morrison et al. [1]_ for connections between excitatory neurons. + See Morrison et al. [2]_ for connections between excitatory neurons. - Dynamics of synaptic weights :math:`J_{ij}(t)` :math:`\forall{}i\in\mathcal{E}, j\in\mathcal{E}`: @@ -581,6 +583,10 @@ Initial conditions parameters +---------------------------------------+------------------------+----------------------+ -.. [1] Morrison A. Aertsen, A. and Diesmann M. 2007. +.. [1] Senk J, Kriener B, Djurfeldt M, Voges N, Jiang H-J, Schüttler L, et al. 2022. + Connectivity concepts in neuronal network modeling. PLoS Comput Biol 18(9): e1010086. + https://doi.org/10.1371/journal.pcbi.1010086 + +.. [2] Morrison A. Aertsen, A. and Diesmann M. 2007. Spike-timing-dependent plasticity in balanced random networks. Neural Computation. 19(6):1437–1467. diff --git a/pynest/examples/ignore_and_fire/NetworkSketch_TwoPopulationNetworkPlastic_converted.svg b/pynest/examples/ignore_and_fire/NetworkSketch_TwoPopulationNetworkPlastic_converted.svg new file mode 100644 index 0000000000..c1fb2183e9 --- /dev/null +++ b/pynest/examples/ignore_and_fire/NetworkSketch_TwoPopulationNetworkPlastic_converted.svg @@ -0,0 +1,609 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + From 3ad57d73b7cde098ee337094bc997f92c1b6873e Mon Sep 17 00:00:00 2001 From: Jessica Mitchell Date: Thu, 25 Jan 2024 11:42:08 +0100 Subject: [PATCH 07/38] move links to admonitions in appropriate section --- pynest/examples/ignore_and_fire/README.rst | 64 +++++++++------------- 1 file changed, 26 insertions(+), 38 deletions(-) diff --git a/pynest/examples/ignore_and_fire/README.rst b/pynest/examples/ignore_and_fire/README.rst index 799e20dfa5..be9c395525 100644 --- a/pynest/examples/ignore_and_fire/README.rst +++ b/pynest/examples/ignore_and_fire/README.rst @@ -1,36 +1,6 @@ -Using ``ignore_and_fire`` neuron for scaling experiments +Scaling experiments using the ``ignore_and_fire`` neuron ======================================================== -File structure ---------------- - - -- :doc:`/auto_examples/ignore_and_fire/model`: an example script using :doc:`ignore_and_fire ` - -- :doc:`Parameter dictionary ` - -- :doc:`Simulation details ` - - -- :doc:`Detailed network model description and - parameters ` - - -- :doc:`Run scaling experiments and generate scaling - figure ` - -- :doc:`Run network simulations to produce spike and synaptic-weight - data shown in figures below ` - -- :doc:`Generate figure below showing spiking activity and synaptic weight - distributions ` - - -Citing this code ----------------- - -If you use this code, we ask you to cite the paper by ??? and the NEST release on Zenodo. - (Non-) Scalability of recurrent neuronal networks ------------------------------------------------- @@ -70,11 +40,7 @@ connections between excitatory neurons. The weight dynamics are described by the spike-timing-dependent plasticity (STDP) model derived by Morrison et al. ( [7]_). The model provides a mechanism underlying the formation of broad distributions of synaptic weights in combination with -asynchronous irregular spiking activity (see figure below). A detailed -description of the network model and parameters are provided -`here `__. -Implementation details can be found -`here `__. +asynchronous irregular spiking activity (see figure below). A variant of this model, the :doc:`hpc_benchmark ` @@ -93,13 +59,20 @@ of the weight distribution (see figure below). Synaptic weights thereby become a sensitive target metric for verification and validation studies. +.. seealso:: + + * :doc:`Detailed description of the network model and parameters ` + * :doc:`Implementation details for the NEST simulation `. + * :doc:`model.py `: network definition and :doc:`parameter_dicts.py `: parameter dictionary + * :doc:`ignore_and_fire ` neuron model documentation + + Comparison between the networks with ``integrate-and-fire`` and ``ignore-and-fire`` dynamics -------------------------------------------------------------------------------------------- The model employed here can be configured into a truly scalable mode by replacing the integrate-and-fire neurons by an ``ignore_and_fire`` -dynamics (for details, see -`here `__). By +dynamics. By doing so, the spike generation dynamics is decoupled from the input integration and the plasticity dynamics; the overall network activity, and, hence, the communication load, is fully controlled by the user. The @@ -125,6 +98,15 @@ Spiking activity (top) and synaptic weight distributions (bottom) of the network with integrate-and-fire (``iaf_psc_alpha_nest``) and ignore-and-fire dynamics (``ignore_and_fire``). Scaling experiments + +.. admonition:: Run script + + To recreate the above figures: + + * :doc:`generate_reference_data.py `: Run network simulations to produce spike and synapstic weight data + * :doc:`generate_reference_figures.py `: Generate figures for spiking activity and synaptic weigh distributions + +Scaling experiments ------------------- The ``ignore_and_fire`` variant of the model permits exact scaling @@ -141,6 +123,12 @@ network size (see figure below). ``ignore-and-fire`` variant of the network model (gray). The in-degree :math:`K=1250` is fixed. +.. admonition:: Run script + + * :doc:`scaling.py `: Run scaling experiments and generate scaling figure + + + References ---------- From 5f6b656320fdc10a330ebd946dceda9cd28c6827 Mon Sep 17 00:00:00 2001 From: Jessica Mitchell Date: Thu, 25 Jan 2024 11:42:37 +0100 Subject: [PATCH 08/38] add LIF to text --- pynest/examples/ignore_and_fire/simulation_details.rst | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/pynest/examples/ignore_and_fire/simulation_details.rst b/pynest/examples/ignore_and_fire/simulation_details.rst index 7abe412c37..d1607fbaa7 100644 --- a/pynest/examples/ignore_and_fire/simulation_details.rst +++ b/pynest/examples/ignore_and_fire/simulation_details.rst @@ -4,9 +4,8 @@ Two population STDP network model simulation Simulation details ------------------ -By default, this implementation is based on the -:doc:`iaf_psc_alpha ` -neuron and the +By default, this implementation is based on the LIF neuron, +:doc:`iaf_psc_alpha ` and the :doc:`stdp_pl_synapse_hom ` synapse models provided in NEST. With ``pars['neuron_model']='ignore_and_fire'``, the model is configured into From f58e6050c3c7e05a75c751618acd4dd67c41e8bc Mon Sep 17 00:00:00 2001 From: Tom Tetzlaff Date: Wed, 28 Feb 2024 12:36:59 +0100 Subject: [PATCH 09/38] model description moved to doc folder; Snakefile for model sketch generation --- ..._TwoPopulationNetworkPlastic_converted.svg | 609 ------------------ ...escription_TwoPopulationNetworkPlastic.rst | 11 +- ...orkSketch_TwoPopulationNetworkPlastic.fodg | 0 ...workSketch_TwoPopulationNetworkPlastic.svg | 335 ++++++++++ .../ignore_and_fire/doc/figures/Snakefile | 33 + 5 files changed, 373 insertions(+), 615 deletions(-) delete mode 100644 pynest/examples/ignore_and_fire/NetworkSketch_TwoPopulationNetworkPlastic_converted.svg rename pynest/examples/ignore_and_fire/{ => doc}/ModelDescription_TwoPopulationNetworkPlastic.rst (98%) rename pynest/examples/ignore_and_fire/{ => doc/figures}/NetworkSketch_TwoPopulationNetworkPlastic.fodg (100%) create mode 100644 pynest/examples/ignore_and_fire/doc/figures/NetworkSketch_TwoPopulationNetworkPlastic.svg create mode 100644 pynest/examples/ignore_and_fire/doc/figures/Snakefile diff --git a/pynest/examples/ignore_and_fire/NetworkSketch_TwoPopulationNetworkPlastic_converted.svg b/pynest/examples/ignore_and_fire/NetworkSketch_TwoPopulationNetworkPlastic_converted.svg deleted file mode 100644 index c1fb2183e9..0000000000 --- a/pynest/examples/ignore_and_fire/NetworkSketch_TwoPopulationNetworkPlastic_converted.svg +++ /dev/null @@ -1,609 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/pynest/examples/ignore_and_fire/ModelDescription_TwoPopulationNetworkPlastic.rst b/pynest/examples/ignore_and_fire/doc/ModelDescription_TwoPopulationNetworkPlastic.rst similarity index 98% rename from pynest/examples/ignore_and_fire/ModelDescription_TwoPopulationNetworkPlastic.rst rename to pynest/examples/ignore_and_fire/doc/ModelDescription_TwoPopulationNetworkPlastic.rst index 5484a35603..9fbedb7e40 100644 --- a/pynest/examples/ignore_and_fire/ModelDescription_TwoPopulationNetworkPlastic.rst +++ b/pynest/examples/ignore_and_fire/doc/ModelDescription_TwoPopulationNetworkPlastic.rst @@ -10,22 +10,21 @@ Summary of network model :stub-columns: 1 * - **Populations** - - excitatory population :math:`\mathcal{E}` inhibitory population :math:`\mathcal{I}`, external Poissonian spike sources :math:`\mathcal{X}` + - excitatory population :math:`\mathcal{E}`, inhibitory population :math:`\mathcal{I}`, external Poissonian spike sources :math:`\mathcal{X}` * - **Connectivity** - sparse random connectivity respecting Dale’s principle * - **Neurons** - leaky integrate-and-fire (LIF) * - **Synapses** - - linear input integration with alpha-shaped postsynaptic currents (PSCs), spike-timing dependent plasticity (STDP) for connections between - excitatory neurons + - linear input integration with alpha-function-shaped postsynaptic currents (PSCs), spike-timing dependent plasticity (STDP) for connections between excitatory neurons * - **Input** - stationary, uncorrelated Poissonian spike trains * - - - .. figure:: NetworkSketch_TwoPopulationNetworkPlastic_converted.svg + - .. figure:: figures/NetworkSketch_TwoPopulationNetworkPlastic.svg - Network sketch. For legend see `Fig. 8 `_ - in Senk et al. [1]_. + Network sketch (see `Fig. 8 `_ + in Senk et al. [1]_). Detailed desciption of network model ------------------------------------ diff --git a/pynest/examples/ignore_and_fire/NetworkSketch_TwoPopulationNetworkPlastic.fodg b/pynest/examples/ignore_and_fire/doc/figures/NetworkSketch_TwoPopulationNetworkPlastic.fodg similarity index 100% rename from pynest/examples/ignore_and_fire/NetworkSketch_TwoPopulationNetworkPlastic.fodg rename to pynest/examples/ignore_and_fire/doc/figures/NetworkSketch_TwoPopulationNetworkPlastic.fodg diff --git a/pynest/examples/ignore_and_fire/doc/figures/NetworkSketch_TwoPopulationNetworkPlastic.svg b/pynest/examples/ignore_and_fire/doc/figures/NetworkSketch_TwoPopulationNetworkPlastic.svg new file mode 100644 index 0000000000..2572a62fb9 --- /dev/null +++ b/pynest/examples/ignore_and_fire/doc/figures/NetworkSketch_TwoPopulationNetworkPlastic.svg @@ -0,0 +1,335 @@ + + + + diff --git a/pynest/examples/ignore_and_fire/doc/figures/Snakefile b/pynest/examples/ignore_and_fire/doc/figures/Snakefile new file mode 100644 index 0000000000..3fb522ed2b --- /dev/null +++ b/pynest/examples/ignore_and_fire/doc/figures/Snakefile @@ -0,0 +1,33 @@ +rule all: + input: + 'NetworkSketch_TwoPopulationNetworkPlastic.svg' + +rule convert_network_sketch_to_pdf: + """ + Convert libreoffice draw sketch (fodg format) to pdf. + """ + input: + 'NetworkSketch_TwoPopulationNetworkPlastic.fodg' + + output: + 'NetworkSketch_TwoPopulationNetworkPlastic.pdf' + + run: + shell('libreoffice --convert-to pdf:draw_pdf_Export {input}') + +rule convert_network_sketch_to_svg: + """ + Convert pdf to svg. + """ + input: + 'NetworkSketch_TwoPopulationNetworkPlastic.pdf' + + output: + 'NetworkSketch_TwoPopulationNetworkPlastic.svg' + + run: + shell('inkscape --export-type=svg {input}') + +rule clean: + run: + shell("rm *.pdf *.svg") From 3f4c7bc7d8376867f82342611c939b3aae42c981 Mon Sep 17 00:00:00 2001 From: Tom Tetzlaff Date: Wed, 28 Feb 2024 12:55:55 +0100 Subject: [PATCH 10/38] files/folder restructured; README file adapted to new file/folder structure --- pynest/examples/ignore_and_fire/README.rst | 27 +++++++++--------- .../{ => code}/generate_reference_data.py | 0 .../{ => code}/generate_reference_figures.py | 0 .../ignore_and_fire/{ => code}/model.py | 0 .../{ => code}/parameter_dicts.py | 0 .../ignore_and_fire/{ => code}/scaling.py | 0 .../{ => doc}/simulation_details.rst | 0 .../ignore_and_fire/figures/README.rst | 2 -- ...etworkPlastic_iaf_connectivity_postsim.png | Bin ...NetworkPlastic_iaf_connectivity_presim.png | Bin ...TwoPopulationNetworkPlastic_iaf_spikes.png | Bin ...etworkPlastic_iaf_weight_distributions.png | Bin ...etworkPlastic_ign_connectivity_postsim.png | Bin ...NetworkPlastic_ign_connectivity_presim.png | Bin ...TwoPopulationNetworkPlastic_ign_spikes.png | Bin ...etworkPlastic_ign_weight_distributions.png | Bin .../ignore_and_fire/{ => figures}/scaling.png | Bin 17 files changed, 13 insertions(+), 16 deletions(-) rename pynest/examples/ignore_and_fire/{ => code}/generate_reference_data.py (100%) rename pynest/examples/ignore_and_fire/{ => code}/generate_reference_figures.py (100%) rename pynest/examples/ignore_and_fire/{ => code}/model.py (100%) rename pynest/examples/ignore_and_fire/{ => code}/parameter_dicts.py (100%) rename pynest/examples/ignore_and_fire/{ => code}/scaling.py (100%) rename pynest/examples/ignore_and_fire/{ => doc}/simulation_details.rst (100%) delete mode 100644 pynest/examples/ignore_and_fire/figures/README.rst rename pynest/examples/ignore_and_fire/{ => figures}/TwoPopulationNetworkPlastic_iaf_connectivity_postsim.png (100%) rename pynest/examples/ignore_and_fire/{ => figures}/TwoPopulationNetworkPlastic_iaf_connectivity_presim.png (100%) rename pynest/examples/ignore_and_fire/{ => figures}/TwoPopulationNetworkPlastic_iaf_spikes.png (100%) rename pynest/examples/ignore_and_fire/{ => figures}/TwoPopulationNetworkPlastic_iaf_weight_distributions.png (100%) rename pynest/examples/ignore_and_fire/{ => figures}/TwoPopulationNetworkPlastic_ign_connectivity_postsim.png (100%) rename pynest/examples/ignore_and_fire/{ => figures}/TwoPopulationNetworkPlastic_ign_connectivity_presim.png (100%) rename pynest/examples/ignore_and_fire/{ => figures}/TwoPopulationNetworkPlastic_ign_spikes.png (100%) rename pynest/examples/ignore_and_fire/{ => figures}/TwoPopulationNetworkPlastic_ign_weight_distributions.png (100%) rename pynest/examples/ignore_and_fire/{ => figures}/scaling.png (100%) diff --git a/pynest/examples/ignore_and_fire/README.rst b/pynest/examples/ignore_and_fire/README.rst index be9c395525..10d84b6e50 100644 --- a/pynest/examples/ignore_and_fire/README.rst +++ b/pynest/examples/ignore_and_fire/README.rst @@ -61,10 +61,10 @@ studies. .. seealso:: - * :doc:`Detailed description of the network model and parameters ` - * :doc:`Implementation details for the NEST simulation `. - * :doc:`model.py `: network definition and :doc:`parameter_dicts.py `: parameter dictionary - * :doc:`ignore_and_fire ` neuron model documentation + * :doc:`Detailed description of the network model and parameters ` + * :doc:`Implementation details for the NEST simulation `. + * :doc:`code/model.py `: NEST implementation of the network model, and :doc:`code/parameter_dicts.py `: Parameter setting + * :doc:`ignore_and_fire ` Neuron model documentation Comparison between the networks with ``integrate-and-fire`` and ``ignore-and-fire`` dynamics @@ -72,8 +72,7 @@ Comparison between the networks with ``integrate-and-fire`` and ``ignore-and-fir The model employed here can be configured into a truly scalable mode by replacing the integrate-and-fire neurons by an ``ignore_and_fire`` -dynamics. By -doing so, the spike generation dynamics is decoupled from the input +dynamics. By doing so, the spike generation dynamics is decoupled from the input integration and the plasticity dynamics; the overall network activity, and, hence, the communication load, is fully controlled by the user. The firing rates and phases of the ``ignore-and-fire`` model are randomly @@ -88,10 +87,10 @@ activity. The plasticity dynamics remains intact (see figure below). ================== ===================== -.. |iaf_spikes| image:: TwoPopulationNetworkPlastic_iaf_spikes.png -.. |iaf_weight| image:: TwoPopulationNetworkPlastic_iaf_weight_distributions.png -.. |ign_spikes| image:: TwoPopulationNetworkPlastic_ign_spikes.png -.. |ign_weight| image:: TwoPopulationNetworkPlastic_ign_weight_distributions.png +.. |iaf_spikes| image:: figures/TwoPopulationNetworkPlastic_iaf_spikes.png +.. |iaf_weight| image:: figures/TwoPopulationNetworkPlastic_iaf_weight_distributions.png +.. |ign_spikes| image:: figures/TwoPopulationNetworkPlastic_ign_spikes.png +.. |ign_weight| image:: figures/TwoPopulationNetworkPlastic_ign_weight_distributions.png Spiking activity (top) and synaptic weight distributions (bottom) of @@ -103,8 +102,8 @@ ignore-and-fire dynamics (``ignore_and_fire``). Scaling experiments To recreate the above figures: - * :doc:`generate_reference_data.py `: Run network simulations to produce spike and synapstic weight data - * :doc:`generate_reference_figures.py `: Generate figures for spiking activity and synaptic weigh distributions + * :doc:`code/generate_reference_data.py `: Run network simulations to produce spike and synapstic weight data + * :doc:`code/generate_reference_figures.py `: Generate figures for spiking activity and synaptic weigh distributions Scaling experiments ------------------- @@ -114,7 +113,7 @@ experiments, without the need for any parameter tuning when changing the network size (see figure below). -.. figure:: scaling.png +.. figure:: figures/scaling.png :scale: 50% Dependence of the simulation time (top), the time and population @@ -125,7 +124,7 @@ network size (see figure below). .. admonition:: Run script - * :doc:`scaling.py `: Run scaling experiments and generate scaling figure + * :doc:`code/scaling.py `: Run scaling experiments and generate scaling figure diff --git a/pynest/examples/ignore_and_fire/generate_reference_data.py b/pynest/examples/ignore_and_fire/code/generate_reference_data.py similarity index 100% rename from pynest/examples/ignore_and_fire/generate_reference_data.py rename to pynest/examples/ignore_and_fire/code/generate_reference_data.py diff --git a/pynest/examples/ignore_and_fire/generate_reference_figures.py b/pynest/examples/ignore_and_fire/code/generate_reference_figures.py similarity index 100% rename from pynest/examples/ignore_and_fire/generate_reference_figures.py rename to pynest/examples/ignore_and_fire/code/generate_reference_figures.py diff --git a/pynest/examples/ignore_and_fire/model.py b/pynest/examples/ignore_and_fire/code/model.py similarity index 100% rename from pynest/examples/ignore_and_fire/model.py rename to pynest/examples/ignore_and_fire/code/model.py diff --git a/pynest/examples/ignore_and_fire/parameter_dicts.py b/pynest/examples/ignore_and_fire/code/parameter_dicts.py similarity index 100% rename from pynest/examples/ignore_and_fire/parameter_dicts.py rename to pynest/examples/ignore_and_fire/code/parameter_dicts.py diff --git a/pynest/examples/ignore_and_fire/scaling.py b/pynest/examples/ignore_and_fire/code/scaling.py similarity index 100% rename from pynest/examples/ignore_and_fire/scaling.py rename to pynest/examples/ignore_and_fire/code/scaling.py diff --git a/pynest/examples/ignore_and_fire/simulation_details.rst b/pynest/examples/ignore_and_fire/doc/simulation_details.rst similarity index 100% rename from pynest/examples/ignore_and_fire/simulation_details.rst rename to pynest/examples/ignore_and_fire/doc/simulation_details.rst diff --git a/pynest/examples/ignore_and_fire/figures/README.rst b/pynest/examples/ignore_and_fire/figures/README.rst deleted file mode 100644 index 9df37adacf..0000000000 --- a/pynest/examples/ignore_and_fire/figures/README.rst +++ /dev/null @@ -1,2 +0,0 @@ -another readme -============== diff --git a/pynest/examples/ignore_and_fire/TwoPopulationNetworkPlastic_iaf_connectivity_postsim.png b/pynest/examples/ignore_and_fire/figures/TwoPopulationNetworkPlastic_iaf_connectivity_postsim.png similarity index 100% rename from pynest/examples/ignore_and_fire/TwoPopulationNetworkPlastic_iaf_connectivity_postsim.png rename to pynest/examples/ignore_and_fire/figures/TwoPopulationNetworkPlastic_iaf_connectivity_postsim.png diff --git a/pynest/examples/ignore_and_fire/TwoPopulationNetworkPlastic_iaf_connectivity_presim.png b/pynest/examples/ignore_and_fire/figures/TwoPopulationNetworkPlastic_iaf_connectivity_presim.png similarity index 100% rename from pynest/examples/ignore_and_fire/TwoPopulationNetworkPlastic_iaf_connectivity_presim.png rename to pynest/examples/ignore_and_fire/figures/TwoPopulationNetworkPlastic_iaf_connectivity_presim.png diff --git a/pynest/examples/ignore_and_fire/TwoPopulationNetworkPlastic_iaf_spikes.png b/pynest/examples/ignore_and_fire/figures/TwoPopulationNetworkPlastic_iaf_spikes.png similarity index 100% rename from pynest/examples/ignore_and_fire/TwoPopulationNetworkPlastic_iaf_spikes.png rename to pynest/examples/ignore_and_fire/figures/TwoPopulationNetworkPlastic_iaf_spikes.png diff --git a/pynest/examples/ignore_and_fire/TwoPopulationNetworkPlastic_iaf_weight_distributions.png b/pynest/examples/ignore_and_fire/figures/TwoPopulationNetworkPlastic_iaf_weight_distributions.png similarity index 100% rename from pynest/examples/ignore_and_fire/TwoPopulationNetworkPlastic_iaf_weight_distributions.png rename to pynest/examples/ignore_and_fire/figures/TwoPopulationNetworkPlastic_iaf_weight_distributions.png diff --git a/pynest/examples/ignore_and_fire/TwoPopulationNetworkPlastic_ign_connectivity_postsim.png b/pynest/examples/ignore_and_fire/figures/TwoPopulationNetworkPlastic_ign_connectivity_postsim.png similarity index 100% rename from pynest/examples/ignore_and_fire/TwoPopulationNetworkPlastic_ign_connectivity_postsim.png rename to pynest/examples/ignore_and_fire/figures/TwoPopulationNetworkPlastic_ign_connectivity_postsim.png diff --git a/pynest/examples/ignore_and_fire/TwoPopulationNetworkPlastic_ign_connectivity_presim.png b/pynest/examples/ignore_and_fire/figures/TwoPopulationNetworkPlastic_ign_connectivity_presim.png similarity index 100% rename from pynest/examples/ignore_and_fire/TwoPopulationNetworkPlastic_ign_connectivity_presim.png rename to pynest/examples/ignore_and_fire/figures/TwoPopulationNetworkPlastic_ign_connectivity_presim.png diff --git a/pynest/examples/ignore_and_fire/TwoPopulationNetworkPlastic_ign_spikes.png b/pynest/examples/ignore_and_fire/figures/TwoPopulationNetworkPlastic_ign_spikes.png similarity index 100% rename from pynest/examples/ignore_and_fire/TwoPopulationNetworkPlastic_ign_spikes.png rename to pynest/examples/ignore_and_fire/figures/TwoPopulationNetworkPlastic_ign_spikes.png diff --git a/pynest/examples/ignore_and_fire/TwoPopulationNetworkPlastic_ign_weight_distributions.png b/pynest/examples/ignore_and_fire/figures/TwoPopulationNetworkPlastic_ign_weight_distributions.png similarity index 100% rename from pynest/examples/ignore_and_fire/TwoPopulationNetworkPlastic_ign_weight_distributions.png rename to pynest/examples/ignore_and_fire/figures/TwoPopulationNetworkPlastic_ign_weight_distributions.png diff --git a/pynest/examples/ignore_and_fire/scaling.png b/pynest/examples/ignore_and_fire/figures/scaling.png similarity index 100% rename from pynest/examples/ignore_and_fire/scaling.png rename to pynest/examples/ignore_and_fire/figures/scaling.png From 3e39bae95c5ca2c8bb0fe5a4fd5a172064a30495 Mon Sep 17 00:00:00 2001 From: Tom Tetzlaff Date: Wed, 28 Feb 2024 18:07:56 +0100 Subject: [PATCH 11/38] implementation and documentation of ignore_and_fire usecase in snakemake --- pynest/examples/ignore_and_fire/Snakefile | 97 ++++++++++++++++++ .../code/generate_reference_data.py | 11 +- .../code/generate_reference_figures.py | 38 ++++--- pynest/examples/ignore_and_fire/code/model.py | 6 +- .../ignore_and_fire/code/parameter_dicts.py | 2 +- .../examples/ignore_and_fire/code/scaling.py | 32 +++--- ...ic_iaf_psc_alpha_connectivity_postsim.png} | Bin ...tic_iaf_psc_alpha_connectivity_presim.png} | Bin ...onNetworkPlastic_iaf_psc_alpha_spikes.png} | Bin ...ic_iaf_psc_alpha_weight_distributions.png} | Bin ..._ignore_and_fire_connectivity_postsim.png} | Bin ...c_ignore_and_fire_connectivity_presim.png} | Bin ...NetworkPlastic_ignore_and_fire_spikes.png} | Bin ..._ignore_and_fire_weight_distributions.png} | Bin .../ignore_and_fire/figures/scaling.png | Bin 91904 -> 92297 bytes .../ignore_and_fire/reference_data/README.rst | 2 - 16 files changed, 145 insertions(+), 43 deletions(-) create mode 100644 pynest/examples/ignore_and_fire/Snakefile rename pynest/examples/ignore_and_fire/figures/{TwoPopulationNetworkPlastic_iaf_connectivity_postsim.png => TwoPopulationNetworkPlastic_iaf_psc_alpha_connectivity_postsim.png} (100%) rename pynest/examples/ignore_and_fire/figures/{TwoPopulationNetworkPlastic_iaf_connectivity_presim.png => TwoPopulationNetworkPlastic_iaf_psc_alpha_connectivity_presim.png} (100%) rename pynest/examples/ignore_and_fire/figures/{TwoPopulationNetworkPlastic_iaf_spikes.png => TwoPopulationNetworkPlastic_iaf_psc_alpha_spikes.png} (100%) rename pynest/examples/ignore_and_fire/figures/{TwoPopulationNetworkPlastic_iaf_weight_distributions.png => TwoPopulationNetworkPlastic_iaf_psc_alpha_weight_distributions.png} (100%) rename pynest/examples/ignore_and_fire/figures/{TwoPopulationNetworkPlastic_ign_connectivity_postsim.png => TwoPopulationNetworkPlastic_ignore_and_fire_connectivity_postsim.png} (100%) rename pynest/examples/ignore_and_fire/figures/{TwoPopulationNetworkPlastic_ign_connectivity_presim.png => TwoPopulationNetworkPlastic_ignore_and_fire_connectivity_presim.png} (100%) rename pynest/examples/ignore_and_fire/figures/{TwoPopulationNetworkPlastic_ign_spikes.png => TwoPopulationNetworkPlastic_ignore_and_fire_spikes.png} (100%) rename pynest/examples/ignore_and_fire/figures/{TwoPopulationNetworkPlastic_ign_weight_distributions.png => TwoPopulationNetworkPlastic_ignore_and_fire_weight_distributions.png} (100%) delete mode 100644 pynest/examples/ignore_and_fire/reference_data/README.rst diff --git a/pynest/examples/ignore_and_fire/Snakefile b/pynest/examples/ignore_and_fire/Snakefile new file mode 100644 index 0000000000..d90cde1ba1 --- /dev/null +++ b/pynest/examples/ignore_and_fire/Snakefile @@ -0,0 +1,97 @@ +rule all: + """ + Creates all figures for use case documentation. + """ + input: + 'figures/TwoPopulationNetworkPlastic_iaf_psc_alpha_spikes.png', + 'figures/TwoPopulationNetworkPlastic_iaf_psc_alpha_weight_distributions.png', + 'figures/TwoPopulationNetworkPlastic_iaf_psc_alpha_connectivity_presim.png', + 'figures/TwoPopulationNetworkPlastic_iaf_psc_alpha_connectivity_postsim.png', + 'figures/TwoPopulationNetworkPlastic_ignore_and_fire_spikes.png', + 'figures/TwoPopulationNetworkPlastic_ignore_and_fire_weight_distributions.png', + 'figures/TwoPopulationNetworkPlastic_ignore_and_fire_connectivity_presim.png', + 'figures/TwoPopulationNetworkPlastic_ignore_and_fire_connectivity_postsim.png', + 'figures/scaling.png' + +rule generate_reference_data_LIF: + """ + Simulate network with LIF neurons, and store spikes and synaptic weights. + """ + input: + 'code/model.py', + 'code/parameter_dicts.py', + 'code/generate_reference_data.py' + output: + temp('.LIF_simulation_done') + run: + shell('cd code; python generate_reference_data.py iaf_psc_alpha; cd ..; touch {output}') + +rule generate_reference_data_IgAF: + """ + Simulate network with ignore-and-fire neurons, and store spikes and synaptic weights. + """ + input: + 'code/model.py', + 'code/parameter_dicts.py', + 'code/generate_reference_data.py' + output: + temp('.IgAF_simulation_done') + run: + shell('cd code; python generate_reference_data.py ignore_and_fire; cd .. ; touch {output}') + +rule generate_reference_figures_LIF: + """ + Create figures showing spiking activity, weight matrices and weight distributions + before and after simulations from data obtained by simulating model + with LIF neurons. + """ + input: + 'code/model.py', + 'code/parameter_dicts.py', + 'code/generate_reference_figures.py', + '.LIF_simulation_done' + output: + 'figures/TwoPopulationNetworkPlastic_iaf_psc_alpha_spikes.png', + 'figures/TwoPopulationNetworkPlastic_iaf_psc_alpha_weight_distributions.png', + 'figures/TwoPopulationNetworkPlastic_iaf_psc_alpha_connectivity_presim.png', + 'figures/TwoPopulationNetworkPlastic_iaf_psc_alpha_connectivity_postsim.png', + run: + shell('cd code; python generate_reference_figures.py iaf_psc_alpha') + +rule generate_reference_figures_IgAF: + """ + Create figures showing spiking activity, weight matrices and weight distributions + before and after simulations from data obtained by simulating model + with ignore_and_fire neurons. + """ + input: + 'code/model.py', + 'code/parameter_dicts.py', + 'code/generate_reference_figures.py', + '.IgAF_simulation_done' + output: + 'figures/TwoPopulationNetworkPlastic_ignore_and_fire_spikes.png', + 'figures/TwoPopulationNetworkPlastic_ignore_and_fire_weight_distributions.png', + 'figures/TwoPopulationNetworkPlastic_ignore_and_fire_connectivity_presim.png', + 'figures/TwoPopulationNetworkPlastic_ignore_and_fire_connectivity_postsim.png', + run: + shell('cd code; python generate_reference_figures.py ignore_and_fire') + +rule scaling_experiment: + """ + Performs scaling experiments with LIF and ignore-and-fire neurons, + and creates figure showing the results. + """ + input: + 'code/model.py', + 'code/parameter_dicts.py', + 'code/scaling.py' + output: + 'figures/scaling.png' + run: + shell('cd code; python scaling.py') +rule clean: + run: + shell("rm figures/*") + shell("rm -rf code/data/*") + diff --git a/pynest/examples/ignore_and_fire/code/generate_reference_data.py b/pynest/examples/ignore_and_fire/code/generate_reference_data.py index a3194410c9..7882c73b9b 100644 --- a/pynest/examples/ignore_and_fire/code/generate_reference_data.py +++ b/pynest/examples/ignore_and_fire/code/generate_reference_data.py @@ -3,14 +3,12 @@ ======================= """ import sys - -sys.path.append(r"../") import model import psutil ################################################# -def generate_reference_data(): +def generate_reference_data(neuron_model='ignore_and_fire'): """ Generate set of reference data and store on disk (spike data and model paramaters). @@ -28,10 +26,15 @@ def generate_reference_data(): """ parameters = model.get_default_parameters() + parameters["neuron_model"] = neuron_model + parameters["record_spikes"] = True # parameters["record_weights"] = True model_instance = model.Model(parameters) + + print("\nneuron model: %s" % model_instance.pars['neuron_model']) + model_instance.create() model_instance.connect() @@ -59,4 +62,4 @@ def generate_reference_data(): ################################################# -generate_reference_data() +generate_reference_data(neuron_model = sys.argv[1]) diff --git a/pynest/examples/ignore_and_fire/code/generate_reference_figures.py b/pynest/examples/ignore_and_fire/code/generate_reference_figures.py index b1cdeafbb4..7e0dde2049 100644 --- a/pynest/examples/ignore_and_fire/code/generate_reference_figures.py +++ b/pynest/examples/ignore_and_fire/code/generate_reference_figures.py @@ -13,7 +13,6 @@ import numpy as np from matplotlib import gridspec - ############################################## def time_and_population_averaged_spike_rate(spikes, time_interval, pop_size): D = time_interval[1] - time_interval[0] @@ -43,7 +42,7 @@ def plot_spikes(spikes, nodes, pars, path="./"): plt.ylim(0, pars["N_rec_spikes"]) plt.xlabel(r"time (ms)") plt.ylabel(r"neuron id") - plt.savefig(path + "/TwoPopulationNetworkPlastic_spikes.png") + plt.savefig(path + "/TwoPopulationNetworkPlastic_%s_spikes.png" % pars['neuron_model']) # def center_axes_ticks(ax): @@ -62,7 +61,7 @@ def plot_spikes(spikes, nodes, pars, path="./"): ################################################# -def plot_connectivity_matrix(W, pop_pre, pop_post, filename_label=None, path="./"): +def plot_connectivity_matrix(W, pop_pre, pop_post, pars, filename_label=None, path="./"): """ Plot connectivity matrix "W" for source and target neurons contained in "pop_pre" and "pop_post", and save figure to file. @@ -98,11 +97,10 @@ def plot_connectivity_matrix(W, pop_pre, pop_post, filename_label=None, path="./ ### plt.subplots_adjust(left=0.12, bottom=0.1, right=0.9, top=0.95, wspace=0.1) - plt.savefig(path + "/TwoPopulationNetworkPlastic_connectivity%s.png" % (filename_label)) - + plt.savefig(path + "/TwoPopulationNetworkPlastic_%s_connectivity%s.png" % (pars['neuron_model'],filename_label)) ################################################# -def plot_weight_distributions(whist_presim, whist_postsim, weights, path="./"): +def plot_weight_distributions(whist_presim, whist_postsim, weights, pars, path="./"): """ Plot distributions of synaptic weights before and after simulation. """ @@ -121,15 +119,17 @@ def plot_weight_distributions(whist_presim, whist_postsim, weights, path="./"): plt.xlim(weights[0], weights[-2]) plt.ylim(5e-5, 3) plt.subplots_adjust(left=0.12, bottom=0.12, right=0.95, top=0.95) - plt.savefig(path + "/TwoPopulationNetworkPlastic_weight_distributions.png") - + plt.savefig(path + "/TwoPopulationNetworkPlastic_%s_weight_distributions.png" % pars['neuron_model']) ################################################# -def generate_reference_figures(): +def generate_reference_figures(neuron_model='ignore_and_fire'): """Generate and store set of reference data""" ## raster plot parameters = model.get_default_parameters() + + parameters["neuron_model"] = neuron_model + parameters["record_spikes"] = True parameters["record_weights"] = True @@ -142,17 +142,18 @@ def generate_reference_figures(): ## load spikes from reference data spikes = model.load_spike_data( - "../reference_data/" + model_instance.pars["data_path"], + model_instance.pars["data_path"], "spikes-%d" % (np.array(model_instance.nodes["spike_recorder"])[0]), ) - plot_spikes(spikes, model_instance.nodes, model_instance.pars, model_instance.pars["data_path"]) + #plot_spikes(spikes, model_instance.nodes, model_instance.pars, model_instance.pars["data_path"]) + plot_spikes(spikes, model_instance.nodes, model_instance.pars, "../figures") ## load connectivity from reference data connectivity_presim = model.load_connectivity_data( - "../reference_data/" + model_instance.pars["data_path"], "connectivity_presim" + model_instance.pars["data_path"], "connectivity_presim" ) connectivity_postsim = model.load_connectivity_data( - "../reference_data/" + model_instance.pars["data_path"], "connectivity_postsim" + model_instance.pars["data_path"], "connectivity_postsim" ) ## create connectivity matrices before and after simulation for a subset of neurons @@ -163,8 +164,10 @@ def generate_reference_figures(): W_postsim, pop_pre, pop_post = model.get_connectivity_matrix(connectivity_postsim, pop_pre, pop_post) ## plot connectivity matrices - plot_connectivity_matrix(W_presim, pop_pre, pop_post, "_presim", model_instance.pars["data_path"]) - plot_connectivity_matrix(W_postsim, pop_pre, pop_post, "_postsim", model_instance.pars["data_path"]) + #plot_connectivity_matrix(W_presim, pop_pre, pop_post, "_presim", model_instance.pars["data_path"]) + #plot_connectivity_matrix(W_postsim, pop_pre, pop_post, "_postsim", model_instance.pars["data_path"]) + plot_connectivity_matrix(W_presim, pop_pre, pop_post, model_instance.pars, "_presim", "../figures") + plot_connectivity_matrix(W_postsim, pop_pre, pop_post, model_instance.pars, "_postsim", "../figures") ## compute weight distributions # weights = np.arange(29.5,34.1,0.05) @@ -173,9 +176,10 @@ def generate_reference_figures(): whist_postsim = model.get_weight_distribution(connectivity_postsim, weights) ## plot weight distributions - plot_weight_distributions(whist_presim, whist_postsim, weights, model_instance.pars["data_path"]) + #plot_weight_distributions(whist_presim, whist_postsim, weights, model_instance.pars["data_path"]) + plot_weight_distributions(whist_presim, whist_postsim, weights, model_instance.pars, "../figures") ################################################# -generate_reference_figures() +generate_reference_figures(neuron_model = sys.argv[1]) diff --git a/pynest/examples/ignore_and_fire/code/model.py b/pynest/examples/ignore_and_fire/code/model.py index 5631aed5b5..6021e51e53 100644 --- a/pynest/examples/ignore_and_fire/code/model.py +++ b/pynest/examples/ignore_and_fire/code/model.py @@ -71,9 +71,11 @@ def __init__(self, parameters): # set parameters derived from base parameters self.__derived_parameters(parameters) + self.pars["data_path"] += "/" + self.pars["neuron_model"] + # create data directory (if necessary) os.system("mkdir -p " + self.pars["data_path"]) - + # initialize NEST kernel nest.ResetKernel() nest.SetKernelStatus( @@ -152,7 +154,7 @@ def __derived_parameters(self, parameters): # number of neurons spikes are recorded from if self.pars["N_rec_spikes"] == "all": self.pars["N_rec_spikes"] = self.pars["N"] - + return ############################################## diff --git a/pynest/examples/ignore_and_fire/code/parameter_dicts.py b/pynest/examples/ignore_and_fire/code/parameter_dicts.py index acf09220ae..5b4b574c00 100644 --- a/pynest/examples/ignore_and_fire/code/parameter_dicts.py +++ b/pynest/examples/ignore_and_fire/code/parameter_dicts.py @@ -76,4 +76,4 @@ pars["print_simulation_progress"] = True # print network time and realtime factor pars["nest_verbosity"] = "M_WARNING" # "M_FATAL", "M_ERROR", "M_WARNING", "M_DEPRECATED", "M_INFO", "M_ALL" -pars["data_path"] = "./" + pars["neuron_model"] +pars["data_path"] = "data" diff --git a/pynest/examples/ignore_and_fire/code/scaling.py b/pynest/examples/ignore_and_fire/code/scaling.py index b81037163e..148f2b04e1 100644 --- a/pynest/examples/ignore_and_fire/code/scaling.py +++ b/pynest/examples/ignore_and_fire/code/scaling.py @@ -1,16 +1,16 @@ """ -Example scaling -=============== +Scaling experiment +================== -* demonstrate scaling experiments with the Two population STDP network testcase, -* illustrate the problems arising with the standard ``integrate-and-fire`` dynamics, and -* describe how to perform exact scaling experiments by using ``ignore_and_fire`` neurons. +* demonstrates scaling experiments with a plastic two-population network, +* illustrates problems arising with standard ``integrate-and-fire`` dynamics, and +* describes how to perform exact scaling experiments by using ``ignore_and_fire`` neurons. """ +import os import sys import time -sys.path.append(r"../") import json import matplotlib.pyplot as plt @@ -20,13 +20,10 @@ ########################################################## ## parameters -neuron_models = ["iaf_psc_alpha", "ignore_and_fire"] -# neuron_models = ["iaf_psc_alpha"] -# neuron_models = ["ignore_and_fire"] -Ns = numpy.arange(1250, 15000, 1250) -# Ns = numpy.arange(13750,15000,1250) +neuron_models = ["iaf_psc_alpha", "ignore_and_fire"] ## neuron models +Ns = numpy.arange(1250, 15000, 1250) ## network sizes -data_path_root = "./data" +data_path_root = "./data" ## root of path to simulation data simulate = True # simulate = False @@ -49,11 +46,12 @@ def run_model(neuron_model, N, data_path_root): parameters["record_spikes"] = True parameters["neuron_model"] = neuron_model ## choose model flavor parameters["N"] = N ## specify scale (network sizes) - parameters["data_path"] = data_path_root + "_" + neuron_model + "_" + str(N) + parameters["data_path"] = data_path_root + "/N" + str(N) ## create and simulate network - start = time.time() model_instance = model.Model(parameters) + + start = time.time() model_instance.create() model_instance.connect() stop = time.time() @@ -179,7 +177,7 @@ def load_data(neuron_model, N, data_path_root): data = {} ## read data from json file - data_path = data_path_root + "_" + neuron_model + "_" + str(N) + data_path = data_path_root + "/N" + str(N) + "/" + neuron_model with open(data_path + "/data.json") as f: data = json.load(f) @@ -202,7 +200,7 @@ def load_data(neuron_model, N, data_path_root): print("%s\n" % neuron_model) for cN, N in enumerate(Ns): - print("\tN = %d" % N) + print("N = %d" % N) if simulate: data = run_model(neuron_model, int(N), data_path_root) @@ -306,4 +304,4 @@ def load_data(neuron_model, N, data_path_root): ax3.legend(loc=1) plt.subplots_adjust(left=0.17, bottom=0.1, right=0.95, top=0.95, hspace=0.1) - plt.savefig("scaling.png") + plt.savefig("../figures/scaling.png") diff --git a/pynest/examples/ignore_and_fire/figures/TwoPopulationNetworkPlastic_iaf_connectivity_postsim.png b/pynest/examples/ignore_and_fire/figures/TwoPopulationNetworkPlastic_iaf_psc_alpha_connectivity_postsim.png similarity index 100% rename from pynest/examples/ignore_and_fire/figures/TwoPopulationNetworkPlastic_iaf_connectivity_postsim.png rename to pynest/examples/ignore_and_fire/figures/TwoPopulationNetworkPlastic_iaf_psc_alpha_connectivity_postsim.png diff --git a/pynest/examples/ignore_and_fire/figures/TwoPopulationNetworkPlastic_iaf_connectivity_presim.png b/pynest/examples/ignore_and_fire/figures/TwoPopulationNetworkPlastic_iaf_psc_alpha_connectivity_presim.png similarity index 100% rename from pynest/examples/ignore_and_fire/figures/TwoPopulationNetworkPlastic_iaf_connectivity_presim.png rename to pynest/examples/ignore_and_fire/figures/TwoPopulationNetworkPlastic_iaf_psc_alpha_connectivity_presim.png diff --git a/pynest/examples/ignore_and_fire/figures/TwoPopulationNetworkPlastic_iaf_spikes.png b/pynest/examples/ignore_and_fire/figures/TwoPopulationNetworkPlastic_iaf_psc_alpha_spikes.png similarity index 100% rename from pynest/examples/ignore_and_fire/figures/TwoPopulationNetworkPlastic_iaf_spikes.png rename to pynest/examples/ignore_and_fire/figures/TwoPopulationNetworkPlastic_iaf_psc_alpha_spikes.png diff --git a/pynest/examples/ignore_and_fire/figures/TwoPopulationNetworkPlastic_iaf_weight_distributions.png b/pynest/examples/ignore_and_fire/figures/TwoPopulationNetworkPlastic_iaf_psc_alpha_weight_distributions.png similarity index 100% rename from pynest/examples/ignore_and_fire/figures/TwoPopulationNetworkPlastic_iaf_weight_distributions.png rename to pynest/examples/ignore_and_fire/figures/TwoPopulationNetworkPlastic_iaf_psc_alpha_weight_distributions.png diff --git a/pynest/examples/ignore_and_fire/figures/TwoPopulationNetworkPlastic_ign_connectivity_postsim.png b/pynest/examples/ignore_and_fire/figures/TwoPopulationNetworkPlastic_ignore_and_fire_connectivity_postsim.png similarity index 100% rename from pynest/examples/ignore_and_fire/figures/TwoPopulationNetworkPlastic_ign_connectivity_postsim.png rename to pynest/examples/ignore_and_fire/figures/TwoPopulationNetworkPlastic_ignore_and_fire_connectivity_postsim.png diff --git a/pynest/examples/ignore_and_fire/figures/TwoPopulationNetworkPlastic_ign_connectivity_presim.png b/pynest/examples/ignore_and_fire/figures/TwoPopulationNetworkPlastic_ignore_and_fire_connectivity_presim.png similarity index 100% rename from pynest/examples/ignore_and_fire/figures/TwoPopulationNetworkPlastic_ign_connectivity_presim.png rename to pynest/examples/ignore_and_fire/figures/TwoPopulationNetworkPlastic_ignore_and_fire_connectivity_presim.png diff --git a/pynest/examples/ignore_and_fire/figures/TwoPopulationNetworkPlastic_ign_spikes.png b/pynest/examples/ignore_and_fire/figures/TwoPopulationNetworkPlastic_ignore_and_fire_spikes.png similarity index 100% rename from pynest/examples/ignore_and_fire/figures/TwoPopulationNetworkPlastic_ign_spikes.png rename to pynest/examples/ignore_and_fire/figures/TwoPopulationNetworkPlastic_ignore_and_fire_spikes.png diff --git a/pynest/examples/ignore_and_fire/figures/TwoPopulationNetworkPlastic_ign_weight_distributions.png b/pynest/examples/ignore_and_fire/figures/TwoPopulationNetworkPlastic_ignore_and_fire_weight_distributions.png similarity index 100% rename from pynest/examples/ignore_and_fire/figures/TwoPopulationNetworkPlastic_ign_weight_distributions.png rename to pynest/examples/ignore_and_fire/figures/TwoPopulationNetworkPlastic_ignore_and_fire_weight_distributions.png diff --git a/pynest/examples/ignore_and_fire/figures/scaling.png b/pynest/examples/ignore_and_fire/figures/scaling.png index 0963ca54a44c81a9aa28258d40e39e4e8e0245cb..fd265fe21eef2c736f6970bc2f1741333e281219 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b/pynest/examples/ignore_and_fire/README.rst index 10d84b6e50..90b782f504 100644 --- a/pynest/examples/ignore_and_fire/README.rst +++ b/pynest/examples/ignore_and_fire/README.rst @@ -62,7 +62,7 @@ studies. .. seealso:: * :doc:`Detailed description of the network model and parameters ` - * :doc:`Implementation details for the NEST simulation `. + * :doc:`NEST implementation details `. * :doc:`code/model.py `: NEST implementation of the network model, and :doc:`code/parameter_dicts.py `: Parameter setting * :doc:`ignore_and_fire ` Neuron model documentation @@ -87,30 +87,19 @@ activity. The plasticity dynamics remains intact (see figure below). ================== ===================== -.. |iaf_spikes| image:: figures/TwoPopulationNetworkPlastic_iaf_spikes.png -.. |iaf_weight| image:: figures/TwoPopulationNetworkPlastic_iaf_weight_distributions.png -.. |ign_spikes| image:: figures/TwoPopulationNetworkPlastic_ign_spikes.png -.. |ign_weight| image:: figures/TwoPopulationNetworkPlastic_ign_weight_distributions.png +.. |iaf_spikes| image:: figures/TwoPopulationNetworkPlastic_iaf_psc_alpha_spikes.png +.. |iaf_weight| image:: figures/TwoPopulationNetworkPlastic_iaf_psc_alpha_weight_distributions.png +.. |ign_spikes| image:: figures/TwoPopulationNetworkPlastic_ignore_and_fire_spikes.png +.. |ign_weight| image:: figures/TwoPopulationNetworkPlastic_ignore_and_fire_weight_distributions.png -Spiking activity (top) and synaptic weight distributions (bottom) of -the network with integrate-and-fire (``iaf_psc_alpha_nest``) and -ignore-and-fire dynamics (``ignore_and_fire``). Scaling experiments +Spiking activity (top) and synaptic weight distributions (bottom) of the network with integrate-and-fire (``iaf_psc_alpha_nest``) and ignore-and-fire dynamics (``ignore_and_fire``). Figures generated using :doc:`code/generate_reference_data.py ` and :doc:`code generate_reference_figures.py `. -.. admonition:: Run script - - To recreate the above figures: - - * :doc:`code/generate_reference_data.py `: Run network simulations to produce spike and synapstic weight data - * :doc:`code/generate_reference_figures.py `: Generate figures for spiking activity and synaptic weigh distributions - Scaling experiments ------------------- -The ``ignore_and_fire`` variant of the model permits exact scaling -experiments, without the need for any parameter tuning when changing the -network size (see figure below). +The ``ignore_and_fire`` variant of the model permits exact scaling experiments, without the need for any parameter tuning when changing the network size (see figure below). .. figure:: figures/scaling.png @@ -120,12 +109,7 @@ network size (see figure below). averaged firing rate (middle) and the synaptic weights (bottom) on the network size :math:`N` for the\ ``integrate-and-fire`` (black) and the ``ignore-and-fire`` variant of the network model (gray). The in-degree - :math:`K=1250` is fixed. - -.. admonition:: Run script - - * :doc:`code/scaling.py `: Run scaling experiments and generate scaling figure - + :math:`K=1250` is fixed. Figure generated using :doc:`code/scaling.py `. From 0dc98dd3e6274a4c1c0258c69b072fea0931810e Mon Sep 17 00:00:00 2001 From: Tom Tetzlaff Date: Wed, 28 Feb 2024 18:27:46 +0100 Subject: [PATCH 13/38] obsolete file/folder removed --- pynest/examples/ignore_and_fire/scaling/README.rst | 2 -- 1 file changed, 2 deletions(-) delete mode 100644 pynest/examples/ignore_and_fire/scaling/README.rst diff --git a/pynest/examples/ignore_and_fire/scaling/README.rst b/pynest/examples/ignore_and_fire/scaling/README.rst deleted file mode 100644 index f39459174a..0000000000 --- a/pynest/examples/ignore_and_fire/scaling/README.rst +++ /dev/null @@ -1,2 +0,0 @@ -readme -====== From 67e113ff1112f785aacf208768c335b1abf19d40 Mon Sep 17 00:00:00 2001 From: Tom Tetzlaff Date: Thu, 29 Feb 2024 14:17:23 +0100 Subject: [PATCH 14/38] revision ignore_and_fire usecase docu --- pynest/examples/ignore_and_fire/README.rst | 83 +++++----------------- 1 file changed, 18 insertions(+), 65 deletions(-) diff --git a/pynest/examples/ignore_and_fire/README.rst b/pynest/examples/ignore_and_fire/README.rst index 90b782f504..8ea786c9ad 100644 --- a/pynest/examples/ignore_and_fire/README.rst +++ b/pynest/examples/ignore_and_fire/README.rst @@ -1,63 +1,23 @@ -Scaling experiments using the ``ignore_and_fire`` neuron -======================================================== - - -(Non-) Scalability of recurrent neuronal networks -------------------------------------------------- - -The verification and validation of simulation architectures (soft- and -hardware) is typically based on models describing networks of neurons. -Ideally, such test-case models are *scalable* with respect to the -network size - -- to foster a comparison between different computing architectures with - different computational resources, -- to be able to extrapolate (up-scale) to networks at brain scale, even - if data constrained and well tested network models at this scale are - not published or existing yet, and -- to be able to study and compare different plasticity mechanisms with - slow dynamics (down-scaling). - -Biological neuronal networks are characterized by a high degree of -recurrency. As shown by van Albada et al. (2015), scaling the number of -nodes or edges in a recurrent neuronal networks generally alters the -network dynamics such as the structure of pairwise correlations. -Preserving certain dynamical features by adjusting other parameters can -only be achieved in limited ranges or exceptional cases. Recurrent -neuronal networks are hence not truly scalable. In this example, we -demonstrate how the ``ignore_and_fire`` can help to perform -systematic scaling experiments with arbitrary types of networks, without -altering the network dynamics. +Exact scaling experiments using the ``ignore_and_fire`` neuron +=============================================================== + +Background: (Non-) Scalability of recurrent neuronal networks +--------------------------------------------------------------- + +The verification and validation of neuronal simulation architectures (soft- and hardware) is typically based on models describing networks of neurons. Ideally, such test-case models are scalable with respect to the network size + +- to foster a comparison between different computing architectures with different computational resources, +- to be able to extrapolate (up-scale) to networks at brain scale, even if data constrained and well tested network models at this scale are not yet published or existing, and +- to be able to study and compare different plasticity mechanisms with slow dynamics (down-scaling). + +Biological neuronal networks are characterized by a high degree of recurrency. As shown by van Albada et al. (2015), scaling the number of nodes or edges in a recurrent neuronal networks generally alters the network dynamics such as the structure of pairwise correlations. Preserving certain dynamical features by adjusting other parameters can only be achieved in limited ranges or exceptional cases. Recurrent neuronal networks are hence not truly scalable. In this example, we demonstrate how the :doc:`ignore_and_fire ` neuron can help to perform exact scaling experiments with arbitrary types of networks. Network model ------------- -In this example, we employ a simple network model describing the -dynamics of a local cortical circuit at the spatial scale of ~1mm within -a single cortical layer. It is derived from the model proposed in -(Brunel [1]_), but accounts for the synaptic weight dynamics for -connections between excitatory neurons. The weight dynamics are -described by the spike-timing-dependent plasticity (STDP) model derived -by Morrison et al. ( [7]_). The model provides a mechanism underlying the -formation of broad distributions of synaptic weights in combination with -asynchronous irregular spiking activity (see figure below). - -A variant of this model, the -:doc:`hpc_benchmark ` -has been used in a number of benchmarking studies, in particular for -weak-scaling experiments (Helias et al., [2]_; Kunkel et al., [3]_; -Ippen et al., [4]_; Kunkel & Schenk, [5]_; Jordan et al., [6]_). Due to -its random homogeneous connectivity, the model represents a hard -benchmarking scenario: each neuron projects with equal probability to -any other neuron in the network. Implementations of this model can -therefore not exploit any spatial connectivity patterns. In contrast to -the model used here, the plasticity dynamics in the ``hpc_benchmark`` is -parameterized such that it has only a weak effect on the synaptic -weights and, hence, the network dynamics. Here, the effect of the -synaptic plasticity is substantial and leads to a significant broadening -of the weight distribution (see figure below). Synaptic weights thereby -become a sensitive target metric for verification and validation -studies. +In this example, we employ a simple network model describing the dynamics of a local cortical circuit at the spatial scale of ~1mm within a single cortical layer. It is derived from the model proposed in (Brunel [1]_), but accounts for the synaptic weight dynamics for connections between excitatory neurons. The weight dynamics are described by the spike-timing-dependent plasticity (STDP) model derived by Morrison et al. ( [7]_). The model provides a mechanism underlying the formation of broad distributions of synaptic weights in combination with asynchronous irregular spiking activity (see figure below). + +A variant of this model, the :doc:`hpc_benchmark `, has been used in a number of benchmarking studies, in particular for weak-scaling experiments (Helias et al., [2]_; Kunkel et al., [3]_; Ippen et al., [4]_; Kunkel & Schenk, [5]_; Jordan et al., [6]_). Due to its random homogeneous connectivity, the model represents a hard benchmarking scenario: each neuron projects with equal probability to any other neuron in the network. Implementations of this model can therefore not exploit any spatial connectivity patterns. In contrast to the model used here, the plasticity dynamics in the ``hpc_benchmark`` is parameterized such that it has only a weak effect on the synaptic weights and, hence, the network dynamics. Here, the effect of the synaptic plasticity is substantial and leads to a significant broadening of the weight distribution (see figure below). Synaptic weights thereby become a sensitive target metric for verification and validation studies. .. seealso:: @@ -70,14 +30,7 @@ studies. Comparison between the networks with ``integrate-and-fire`` and ``ignore-and-fire`` dynamics -------------------------------------------------------------------------------------------- -The model employed here can be configured into a truly scalable mode by -replacing the integrate-and-fire neurons by an ``ignore_and_fire`` -dynamics. By doing so, the spike generation dynamics is decoupled from the input -integration and the plasticity dynamics; the overall network activity, -and, hence, the communication load, is fully controlled by the user. The -firing rates and phases of the ``ignore-and-fire`` model are randomly -drawn from uniform distributions to guarantee asynchronous spiking -activity. The plasticity dynamics remains intact (see figure below). +The model employed here can be configured into a truly scalable mode by replacing the integrate-and-fire neurons by an :doc:`ignore_and_fire ` dynamics. By doing so, the spike generation dynamics is decoupled from the input integration and the plasticity dynamics; the overall network activity, and, hence, the communication load, is fully controlled by the user. The firing rates and phases of the :doc:`ignore_and_fire ` model are randomly drawn from uniform distributions to guarantee asynchronous spiking activity. The plasticity dynamics remains intact (see figure below). ================== ===================== ``iaf_psc_alpha`` ``ignore_and_fire`` @@ -93,7 +46,7 @@ activity. The plasticity dynamics remains intact (see figure below). .. |ign_weight| image:: figures/TwoPopulationNetworkPlastic_ignore_and_fire_weight_distributions.png -Spiking activity (top) and synaptic weight distributions (bottom) of the network with integrate-and-fire (``iaf_psc_alpha_nest``) and ignore-and-fire dynamics (``ignore_and_fire``). Figures generated using :doc:`code/generate_reference_data.py ` and :doc:`code generate_reference_figures.py `. +Spiking activity (top) and synaptic weight distributions (bottom) of the network with integrate-and-fire (``iaf_psc_alpha_nest``) and :doc:`ignore_and_fire ` dynamics (``ignore_and_fire``). Figures generated using :doc:`code/generate_reference_data.py ` and :doc:`code generate_reference_figures.py `. Scaling experiments From b993792b092a66bcc74c6e8a00e821fffc9c8dbf Mon Sep 17 00:00:00 2001 From: Tom Tetzlaff Date: Thu, 29 Feb 2024 14:31:25 +0100 Subject: [PATCH 15/38] revision ignore_and_fire usecase docu --- pynest/examples/ignore_and_fire/README.rst | 17 ++++++++++------- .../examples/ignore_and_fire/code/scaling.py | 4 ++-- .../ignore_and_fire/figures/scaling.png | Bin 92297 -> 88888 bytes 3 files changed, 12 insertions(+), 9 deletions(-) diff --git a/pynest/examples/ignore_and_fire/README.rst b/pynest/examples/ignore_and_fire/README.rst index 8ea786c9ad..c5c3a8dfea 100644 --- a/pynest/examples/ignore_and_fire/README.rst +++ b/pynest/examples/ignore_and_fire/README.rst @@ -19,13 +19,6 @@ In this example, we employ a simple network model describing the dynamics of a l A variant of this model, the :doc:`hpc_benchmark `, has been used in a number of benchmarking studies, in particular for weak-scaling experiments (Helias et al., [2]_; Kunkel et al., [3]_; Ippen et al., [4]_; Kunkel & Schenk, [5]_; Jordan et al., [6]_). Due to its random homogeneous connectivity, the model represents a hard benchmarking scenario: each neuron projects with equal probability to any other neuron in the network. Implementations of this model can therefore not exploit any spatial connectivity patterns. In contrast to the model used here, the plasticity dynamics in the ``hpc_benchmark`` is parameterized such that it has only a weak effect on the synaptic weights and, hence, the network dynamics. Here, the effect of the synaptic plasticity is substantial and leads to a significant broadening of the weight distribution (see figure below). Synaptic weights thereby become a sensitive target metric for verification and validation studies. -.. seealso:: - - * :doc:`Detailed description of the network model and parameters ` - * :doc:`NEST implementation details `. - * :doc:`code/model.py `: NEST implementation of the network model, and :doc:`code/parameter_dicts.py `: Parameter setting - * :doc:`ignore_and_fire ` Neuron model documentation - Comparison between the networks with ``integrate-and-fire`` and ``ignore-and-fire`` dynamics -------------------------------------------------------------------------------------------- @@ -101,6 +94,16 @@ References plasticity in balanced random networks. Neural Computation 19(6):1437-1467 ` + * :doc:`NEST implementation details `. + * :doc:`code/model.py `: NEST implementation of the network model + * :doc:`code/parameter_dicts.py `: parameter setting + * :doc:`ignore_and_fire model documentation ` + + .. toctree:: :hidden: :glob: diff --git a/pynest/examples/ignore_and_fire/code/scaling.py b/pynest/examples/ignore_and_fire/code/scaling.py index 148f2b04e1..aae8e1d525 100644 --- a/pynest/examples/ignore_and_fire/code/scaling.py +++ b/pynest/examples/ignore_and_fire/code/scaling.py @@ -26,7 +26,7 @@ data_path_root = "./data" ## root of path to simulation data simulate = True -# simulate = False +#simulate = False analyse = True # analyse = False @@ -265,7 +265,7 @@ def load_data(neuron_model, N, data_path_root): ax1.set_xlim(Ns[0], Ns[-1]) ax1.set_xticklabels([]) ax1.set_ylabel(r"simulation time (s)") - ax1.set_title(r"fixed in-degree $K=1250$") + #ax1.set_title(r"fixed in-degree $K=1250$") ## firing rate ax2.plot( diff --git 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z*0V@JLE!H0=`oX_P7>^ixObB-ZR?k!YjPnKoBrfKT-tft@`w`p;=-@2w_$jlaZSW(y2RN247ezVwu~AhH&ey^OJgJlo ztFu?c|KX&VI^GtUUjC2tDcS&bZmvB9^NTBP^-9%S8G2bRVPe?@0HEFb&1Z+7NG-^l zfBfm(d+_AEgWBVqFF`6vzopGHYMZ|c#?@!Urw7M6Ilk9GYQ<=AKy zcIruEtN0VKN&y)`dv79hvb*_YAYCX*V} zXHfg`g;bznPulT6jc5CC#ls3&-9=CfVIhhBTH#l zzKKv`!1`1q0e*N{fj#)v66jB)El5o|gm(U_5lOKptM-J|t&~**;Qp(zRyPGZdujF? z%! zh|Lq(P!r@IW0&NrW0hm3EDjoICLI0!bz)PKQWDz<%<00}*z5?U^8hK4{@oW!zn<7} z>*d*Yja?ckjOr;53MA&+PUwarOrjFmw{M?nvy+wMfrnjX_Y~xlT2WK^WE)S!%SiaKU ScLf#x$jhq8WJ;gC`TqfNYY0>T From d87245bdb93236fc04a33c40526c0f45c6f921f8 Mon Sep 17 00:00:00 2001 From: Tom Tetzlaff Date: Thu, 29 Feb 2024 15:04:17 +0100 Subject: [PATCH 16/38] revision figures for ignore_and_fire usecase --- .../code/generate_reference_figures.py | 27 +++++++++++--- .../examples/ignore_and_fire/code/scaling.py | 35 ++++++++++-------- ...tic_iaf_psc_alpha_connectivity_postsim.png | Bin 27212 -> 43799 bytes ...stic_iaf_psc_alpha_connectivity_presim.png | Bin 23287 -> 37964 bytes ...ionNetworkPlastic_iaf_psc_alpha_spikes.png | Bin 487079 -> 1078955 bytes ...tic_iaf_psc_alpha_weight_distributions.png | Bin 26768 -> 54309 bytes ...c_ignore_and_fire_connectivity_postsim.png | Bin 27800 -> 44558 bytes ...ic_ignore_and_fire_connectivity_presim.png | Bin 23345 -> 37926 bytes ...nNetworkPlastic_ignore_and_fire_spikes.png | Bin 486761 -> 1077123 bytes ...c_ignore_and_fire_weight_distributions.png | Bin 26813 -> 54032 bytes .../ignore_and_fire/figures/scaling.png | Bin 88888 -> 89119 bytes 11 files changed, 42 insertions(+), 20 deletions(-) diff --git a/pynest/examples/ignore_and_fire/code/generate_reference_figures.py b/pynest/examples/ignore_and_fire/code/generate_reference_figures.py index 7e0dde2049..322e5ea204 100644 --- a/pynest/examples/ignore_and_fire/code/generate_reference_figures.py +++ b/pynest/examples/ignore_and_fire/code/generate_reference_figures.py @@ -33,15 +33,17 @@ def plot_spikes(spikes, nodes, pars, path="./"): rate = time_and_population_averaged_spike_rate(spikes, (0.0, pars["T"]), pars["N_rec_spikes"]) # plot spiking activity - plt.figure(num=1, figsize=(7, 5)) + plt.figure(num=1) plt.clf() plt.title(r"time and population averaged firing rate: $\nu=%.2f$ spikes/s" % rate) - plt.plot(spikes[:, 1], spikes[:, 0], "o", ms=0.5, lw=0, mfc="k", mec="k", alpha=0.3, rasterized=True) + plt.plot(spikes[:, 1], spikes[:, 0], "o", ms=1, lw=0, mfc="k", mec="k", mew=0, alpha=0.2, rasterized=True) plt.xlim(0, pars["T"]) plt.ylim(0, pars["N_rec_spikes"]) plt.xlabel(r"time (ms)") plt.ylabel(r"neuron id") + + plt.subplots_adjust(bottom=0.14,top=0.9,left=0.15,right=0.95) plt.savefig(path + "/TwoPopulationNetworkPlastic_%s_spikes.png" % pars['neuron_model']) @@ -71,7 +73,8 @@ def plot_connectivity_matrix(W, pop_pre, pop_post, pars, filename_label=None, pa wmax = 150 print("\nPlotting connectivity matrix...") - fig = plt.figure(num=2, figsize=(6, 5)) + + fig = plt.figure(num=2) plt.clf() gs = gridspec.GridSpec(1, 2, width_ratios=[15, 1]) @@ -105,7 +108,8 @@ def plot_weight_distributions(whist_presim, whist_postsim, weights, pars, path=" Plot distributions of synaptic weights before and after simulation. """ print("\nPlotting weight distributions...") - fig = plt.figure(num=3, figsize=(6, 4)) + + fig = plt.figure(num=3) plt.clf() lw = 3 clr = ["0.6", "0.0"] @@ -118,7 +122,8 @@ def plot_weight_distributions(whist_presim, whist_postsim, weights, pars, path=" plt.ylabel(r"rel. frequency") plt.xlim(weights[0], weights[-2]) plt.ylim(5e-5, 3) - plt.subplots_adjust(left=0.12, bottom=0.12, right=0.95, top=0.95) + #plt.subplots_adjust(left=0.12, bottom=0.12, right=0.95, top=0.95) + plt.subplots_adjust(bottom=0.14,top=0.9,left=0.15,right=0.95) plt.savefig(path + "/TwoPopulationNetworkPlastic_%s_weight_distributions.png" % pars['neuron_model']) ################################################# @@ -182,4 +187,16 @@ def generate_reference_figures(neuron_model='ignore_and_fire'): ################################################# +from matplotlib import rcParams +rcParams['figure.figsize'] = (4,3) +rcParams['figure.dpi'] = 300 +rcParams['font.family'] = 'sans-serif' +rcParams['font.size'] = 8 +rcParams['legend.fontsize'] = 8 +rcParams['axes.titlesize'] = 8 +rcParams['axes.labelsize'] = 8 +rcParams['ytick.labelsize'] = 8 +rcParams['xtick.labelsize'] = 8 +rcParams['text.usetex'] = True + generate_reference_figures(neuron_model = sys.argv[1]) diff --git a/pynest/examples/ignore_and_fire/code/scaling.py b/pynest/examples/ignore_and_fire/code/scaling.py index aae8e1d525..2e455d9f03 100644 --- a/pynest/examples/ignore_and_fire/code/scaling.py +++ b/pynest/examples/ignore_and_fire/code/scaling.py @@ -26,7 +26,7 @@ data_path_root = "./data" ## root of path to simulation data simulate = True -#simulate = False +simulate = False analyse = True # analyse = False @@ -222,17 +222,21 @@ def load_data(neuron_model, N, data_path_root): if analyse: ## plotting - fig = plt.figure(num=3, figsize=(3, 4), dpi=300) - plt.clf() - plt.rcParams.update( - { - "text.usetex": True, - "font.size": 8, - "axes.titlesize": 8, - "legend.fontsize": 8, - } - ) + from matplotlib import rcParams + rcParams['figure.figsize'] = (3,4) + rcParams['figure.dpi'] = 300 + rcParams['font.family'] = 'sans-serif' + rcParams['font.size'] = 8 + rcParams['legend.fontsize'] = 8 + rcParams['axes.titlesize'] = 8 + rcParams['axes.labelsize'] = 8 + rcParams['ytick.labelsize'] = 8 + rcParams['xtick.labelsize'] = 8 + rcParams['text.usetex'] = True + + fig = plt.figure(num=3) + plt.clf() gs = gridspec.GridSpec(3, 1) @@ -260,22 +264,23 @@ def load_data(neuron_model, N, data_path_root): ms=ms, lw=lw, color=clrs[cm], - label=r"%s" % neuron_model, + label=r"\texttt{%s}" % neuron_model, ) ax1.set_xlim(Ns[0], Ns[-1]) ax1.set_xticklabels([]) ax1.set_ylabel(r"simulation time (s)") #ax1.set_title(r"fixed in-degree $K=1250$") - + ax1.legend(loc=2) + ## firing rate ax2.plot( - Ns, rate[cm, :], "-o", mfc=clrs[cm], mec=clrs[cm], ms=ms, lw=lw, color=clrs[cm], label=r"%s" % neuron_model + Ns, rate[cm, :], "-o", mfc=clrs[cm], mec=clrs[cm], ms=ms, lw=lw, color=clrs[cm], label=r"\texttt{%s}" % neuron_model ) 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As shown by van Albada et al. (2015), scaling the number of nodes or edges in a recurrent neuronal networks generally alters the network dynamics such as the structure of pairwise correlations. Preserving certain dynamical features by adjusting other parameters can only be achieved in limited ranges or exceptional cases. Recurrent neuronal networks are hence not truly scalable. In this example, we demonstrate how the :doc:`ignore_and_fire ` neuron can help to perform exact scaling experiments with arbitrary types of networks. +Biological neuronal networks are characterized by a high degree of recurrency. As shown in [1]_, scaling the number of nodes or edges in a recurrent neuronal networks generally alters the network dynamics, such as the average activity level or the structure of correlations. Preserving certain dynamical features by adjusting other parameters can only be achieved in limited ranges or exceptional cases. Recurrent neuronal networks are hence not truly scalable. In this example, we demonstrate how the :doc:`ignore_and_fire ` neuron can help to perform exact scaling experiments with arbitrary types of networks. Network model ------------- -In this example, we employ a simple network model describing the dynamics of a local cortical circuit at the spatial scale of ~1mm within a single cortical layer. It is derived from the model proposed in (Brunel [1]_), but accounts for the synaptic weight dynamics for connections between excitatory neurons. The weight dynamics are described by the spike-timing-dependent plasticity (STDP) model derived by Morrison et al. ( [7]_). The model provides a mechanism underlying the formation of broad distributions of synaptic weights in combination with asynchronous irregular spiking activity (see figure below). +In this example, we employ a simple network model describing the dynamics of a local cortical circuit at the spatial scale of ~1mm within a single cortical layer. It is derived from the model proposed in [2]_, but accounts for the synaptic weight dynamics for connections between excitatory neurons. The weight dynamics are described by the spike-timing-dependent plasticity (STDP) model derived in [8]_. The model provides a mechanism underlying the formation of broad distributions of synaptic weights in combination with asynchronous irregular spiking activity (see figure below). -A variant of this model, the :doc:`hpc_benchmark `, has been used in a number of benchmarking studies, in particular for weak-scaling experiments (Helias et al., [2]_; Kunkel et al., [3]_; Ippen et al., [4]_; Kunkel & Schenk, [5]_; Jordan et al., [6]_). Due to its random homogeneous connectivity, the model represents a hard benchmarking scenario: each neuron projects with equal probability to any other neuron in the network. Implementations of this model can therefore not exploit any spatial connectivity patterns. In contrast to the model used here, the plasticity dynamics in the ``hpc_benchmark`` is parameterized such that it has only a weak effect on the synaptic weights and, hence, the network dynamics. Here, the effect of the synaptic plasticity is substantial and leads to a significant broadening of the weight distribution (see figure below). Synaptic weights thereby become a sensitive target metric for verification and validation studies. +A variant of this model, the :doc:`hpc_benchmark `, has been used in a number of benchmarking studies, in particular for weak-scaling experiments ([3]_, [4]_, [5]_, [6]_, [7]_). Due to its random homogeneous connectivity, the model represents a hard benchmarking scenario: each neuron projects with equal probability to any other neuron in the network. Implementations of this model can therefore not exploit any spatial connectivity patterns. In contrast to the model used here, the plasticity dynamics in the ``hpc_benchmark`` is parameterized such that it has only a weak effect on the synaptic weights and, hence, the network dynamics. Here, the effect of the synaptic plasticity is substantial and leads to a significant broadening of the weight distribution (see figure below). Synaptic weights thereby become a sensitive target metric for verification and validation studies. Comparison between the networks with ``integrate-and-fire`` and ``ignore-and-fire`` dynamics @@ -39,20 +39,22 @@ The model employed here can be configured into a truly scalable mode by replacin .. |ign_weight| image:: figures/TwoPopulationNetworkPlastic_ignore_and_fire_weight_distributions.png -Spiking activity (top) and synaptic weight distributions (bottom) of the network with integrate-and-fire (``iaf_psc_alpha_nest``) and :doc:`ignore_and_fire ` dynamics (``ignore_and_fire``). Figures generated using :doc:`code/generate_reference_data.py ` and :doc:`code generate_reference_figures.py `. +Spiking activity (top) and distributions of excitatory synaptic weights (bottom) for the network with integrate-and-fire (``iaf_psc_alpha_nest``) and :doc:`ignore_and_fire ` dynamics (``ignore_and_fire``). Figures generated using :doc:`code/generate_reference_data.py ` and :doc:`code generate_reference_figures.py `. Scaling experiments ------------------- -The ``ignore_and_fire`` variant of the model permits exact scaling experiments, without the need for any parameter tuning when changing the network size (see figure below). +The ``ignore_and_fire`` variant of the network model permits exact scaling experiments, without the need for any parameter tuning when changing the network size (see figure below). We demonstrate this here by simulating the network for various network sizes between :math:`N=1250` and :math:`N=13750`. +The number of incoming connections per neuron, the in-degree, is kept constant at :math:`K=1250`. +The total number of connections hence scales linearly with :math:`N=1250`. For each simulation, we monitor the simulation (wall clock) time, the time and population averaged firing rate, and the mean standard deviation of the excitatory synaptic weights at the end of the simulation. .. figure:: figures/scaling.png - :scale: 50% +# :scale: 50% Dependence of the simulation time (top), the time and population - averaged firing rate (middle) and the synaptic weights (bottom) on the + averaged firing rate (middle) and the excitatory synaptic weights (bottom) on the network size :math:`N` for the\ ``integrate-and-fire`` (black) and the ``ignore-and-fire`` variant of the network model (gray). The in-degree :math:`K=1250` is fixed. Figure generated using :doc:`code/scaling.py `. @@ -62,37 +64,40 @@ The ``ignore_and_fire`` variant of the model permits exact scaling experiments, References ---------- -.. [1] Brunel (2000). Dynamics of networks of randomly connected excitatory +.. [1] Van Albada S J, Helias M, Diesmann M (2015). Scalability of asynchronous + networks is limited by one-to-one mapping between effective connectivity + and correlations. PLoS computational biology, 11(9), e1004490. + + +.. [2] Brunel N (2000). Dynamics of networks of randomly connected excitatory and inhibitory spiking neurons. Journal of Physiology-Paris - 94(5-6):445-463. - doi:10.1023/A:1008925309027 + 94(5-6):445-463. -.. [2] Helias M, Kunkel S, Masumoto G, Igarashi J, Eppler JM, Ishii S, Fukai +.. [3] Helias M, Kunkel S, Masumoto G, Igarashi J, Eppler JM, Ishii S, Fukai T, Morrison A, Diesmann M (2012). Supercomputers ready for use as discovery machines for neuroscience. Frontiers in Neuroinformatics - 6:26 Date: Thu, 29 Feb 2024 15:30:10 +0100 Subject: [PATCH 18/38] revision docu ignore_and_fire usecase --- pynest/examples/ignore_and_fire/README.rst | 8 ++------ 1 file changed, 2 insertions(+), 6 deletions(-) diff --git a/pynest/examples/ignore_and_fire/README.rst b/pynest/examples/ignore_and_fire/README.rst index 0625c5341b..df996630e7 100644 --- a/pynest/examples/ignore_and_fire/README.rst +++ b/pynest/examples/ignore_and_fire/README.rst @@ -39,7 +39,7 @@ The model employed here can be configured into a truly scalable mode by replacin .. |ign_weight| image:: figures/TwoPopulationNetworkPlastic_ignore_and_fire_weight_distributions.png -Spiking activity (top) and distributions of excitatory synaptic weights (bottom) for the network with integrate-and-fire (``iaf_psc_alpha_nest``) and :doc:`ignore_and_fire ` dynamics (``ignore_and_fire``). Figures generated using :doc:`code/generate_reference_data.py ` and :doc:`code generate_reference_figures.py `. + Spiking activity (top) and distributions of excitatory synaptic weights (bottom) for the network with integrate-and-fire (``iaf_psc_alpha_nest``) and :doc:`ignore_and_fire ` dynamics (``ignore_and_fire``). Figures generated using :doc:`code/generate_reference_data.py ` and :doc:`code generate_reference_figures.py `. Scaling experiments @@ -53,11 +53,7 @@ The total number of connections hence scales linearly with :math:`N=1250`. For e .. figure:: figures/scaling.png # :scale: 50% - Dependence of the simulation time (top), the time and population - averaged firing rate (middle) and the excitatory synaptic weights (bottom) on the - network size :math:`N` for the\ ``integrate-and-fire`` (black) and the - ``ignore-and-fire`` variant of the network model (gray). The in-degree - :math:`K=1250` is fixed. Figure generated using :doc:`code/scaling.py `. + Dependence of the simulation time (top), the time and population averaged firing rate (middle) and the excitatory synaptic weights (bottom) on the network size :math:`N` for the\ ``integrate-and-fire`` (black) and the ``ignore-and-fire`` variant of the network model (gray). The in-degree :math:`K=1250` is fixed. Figure generated using :doc:`code/scaling.py `. From 94f8a3f6c4172155be54d5bb5057a9c2c6bcf1cf Mon Sep 17 00:00:00 2001 From: Tom Tetzlaff Date: Thu, 29 Feb 2024 15:31:17 +0100 Subject: [PATCH 19/38] revision docu ignore_and_fire usecase --- pynest/examples/ignore_and_fire/README.rst | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/pynest/examples/ignore_and_fire/README.rst b/pynest/examples/ignore_and_fire/README.rst index df996630e7..d0b739e785 100644 --- a/pynest/examples/ignore_and_fire/README.rst +++ b/pynest/examples/ignore_and_fire/README.rst @@ -39,6 +39,7 @@ The model employed here can be configured into a truly scalable mode by replacin .. |ign_weight| image:: figures/TwoPopulationNetworkPlastic_ignore_and_fire_weight_distributions.png + Spiking activity (top) and distributions of excitatory synaptic weights (bottom) for the network with integrate-and-fire (``iaf_psc_alpha_nest``) and :doc:`ignore_and_fire ` dynamics (``ignore_and_fire``). Figures generated using :doc:`code/generate_reference_data.py ` and :doc:`code generate_reference_figures.py `. @@ -51,7 +52,7 @@ The total number of connections hence scales linearly with :math:`N=1250`. For e .. figure:: figures/scaling.png -# :scale: 50% + :scale: 50% Dependence of the simulation time (top), the time and population averaged firing rate (middle) and the excitatory synaptic weights (bottom) on the network size :math:`N` for the\ ``integrate-and-fire`` (black) and the ``ignore-and-fire`` variant of the network model (gray). The in-degree :math:`K=1250` is fixed. Figure generated using :doc:`code/scaling.py `. From 5d30b2d036e44dfe5bc5a22dbc99f2be921ee5c0 Mon Sep 17 00:00:00 2001 From: Tom Tetzlaff Date: Thu, 29 Feb 2024 15:31:49 +0100 Subject: [PATCH 20/38] revision docu ignore_and_fire usecase --- pynest/examples/ignore_and_fire/README.rst | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/pynest/examples/ignore_and_fire/README.rst b/pynest/examples/ignore_and_fire/README.rst index d0b739e785..a3b85781fb 100644 --- a/pynest/examples/ignore_and_fire/README.rst +++ b/pynest/examples/ignore_and_fire/README.rst @@ -39,8 +39,7 @@ The model employed here can be configured into a truly scalable mode by replacin .. |ign_weight| image:: figures/TwoPopulationNetworkPlastic_ignore_and_fire_weight_distributions.png - - Spiking activity (top) and distributions of excitatory synaptic weights (bottom) for the network with integrate-and-fire (``iaf_psc_alpha_nest``) and :doc:`ignore_and_fire ` dynamics (``ignore_and_fire``). Figures generated using :doc:`code/generate_reference_data.py ` and :doc:`code generate_reference_figures.py `. +Spiking activity (top) and distributions of excitatory synaptic weights (bottom) for the network with integrate-and-fire (``iaf_psc_alpha_nest``) and :doc:`ignore_and_fire ` dynamics (``ignore_and_fire``). Figures generated using :doc:`code/generate_reference_data.py ` and :doc:`code generate_reference_figures.py `. Scaling experiments @@ -54,7 +53,7 @@ The total number of connections hence scales linearly with :math:`N=1250`. For e .. figure:: figures/scaling.png :scale: 50% - Dependence of the simulation time (top), the time and population averaged firing rate (middle) and the excitatory synaptic weights (bottom) on the network size :math:`N` for the\ ``integrate-and-fire`` (black) and the ``ignore-and-fire`` variant of the network model (gray). The in-degree :math:`K=1250` is fixed. Figure generated using :doc:`code/scaling.py `. +Dependence of the simulation time (top), the time and population averaged firing rate (middle) and the excitatory synaptic weights (bottom) on the network size :math:`N` for the\ ``integrate-and-fire`` (black) and the ``ignore-and-fire`` variant of the network model (gray). The in-degree :math:`K=1250` is fixed. Figure generated using :doc:`code/scaling.py `. From 4245373380ebd9f2e8a743867c28d330a3a97bb9 Mon Sep 17 00:00:00 2001 From: Tom Tetzlaff Date: Thu, 29 Feb 2024 15:32:17 +0100 Subject: [PATCH 21/38] revision docu ignore_and_fire usecase --- pynest/examples/ignore_and_fire/README.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pynest/examples/ignore_and_fire/README.rst b/pynest/examples/ignore_and_fire/README.rst index a3b85781fb..b94d2b3fef 100644 --- a/pynest/examples/ignore_and_fire/README.rst +++ b/pynest/examples/ignore_and_fire/README.rst @@ -51,7 +51,7 @@ The total number of connections hence scales linearly with :math:`N=1250`. For e .. figure:: figures/scaling.png - :scale: 50% + :scale: 20% Dependence of the simulation time (top), the time and population averaged firing rate (middle) and the excitatory synaptic weights (bottom) on the network size :math:`N` for the\ ``integrate-and-fire`` (black) and the ``ignore-and-fire`` variant of the network model (gray). The in-degree :math:`K=1250` is fixed. Figure generated using :doc:`code/scaling.py `. From b0c80740fdb9661409b3423f90e9adde580a3692 Mon Sep 17 00:00:00 2001 From: Tom Tetzlaff Date: Thu, 29 Feb 2024 16:00:50 +0100 Subject: [PATCH 22/38] revision docu ignore_and_fire usecase --- pynest/examples/ignore_and_fire/README.rst | 13 +++++++++++-- 1 file changed, 11 insertions(+), 2 deletions(-) diff --git a/pynest/examples/ignore_and_fire/README.rst b/pynest/examples/ignore_and_fire/README.rst index b94d2b3fef..7670503459 100644 --- a/pynest/examples/ignore_and_fire/README.rst +++ b/pynest/examples/ignore_and_fire/README.rst @@ -49,13 +49,21 @@ The ``ignore_and_fire`` variant of the network model permits exact scaling exper The number of incoming connections per neuron, the in-degree, is kept constant at :math:`K=1250`. The total number of connections hence scales linearly with :math:`N=1250`. For each simulation, we monitor the simulation (wall clock) time, the time and population averaged firing rate, and the mean standard deviation of the excitatory synaptic weights at the end of the simulation. +For the integrate-and-fire variant of the network model, the firing rate and the synaptic-weight distribution depend on the network size :math:`N`. In particular, for small :math:`N`, the firing rates and the synaptic weights increase due to the denser connectivity. + +For the ignore-and-fire version, the dynamics is independent of the network size. The average firing rate is --by definition-- constant. As the firing phases of the ignore-and-fire neurons are chosen randomly, the spiking activity is asynchronous, irrespective of the connection density. As a consequence, the distribution of synaptic weights (which is shaped by correlations between neurons) remains constant, too. The overall communication load, i.e., the total number of spikes that need to be sent across the network within each time interval, is hence fully controlled by the user. .. figure:: figures/scaling.png - :scale: 20% + :scale: 50% Dependence of the simulation time (top), the time and population averaged firing rate (middle) and the excitatory synaptic weights (bottom) on the network size :math:`N` for the\ ``integrate-and-fire`` (black) and the ``ignore-and-fire`` variant of the network model (gray). The in-degree :math:`K=1250` is fixed. Figure generated using :doc:`code/scaling.py `. +Running the simulations +------------------------ + +The workflows underlying the numerical experiments shown above are documented in `Snakefile `. + References ---------- @@ -98,8 +106,9 @@ References .. seealso:: + * :doc:`Snakefile `: simulation workflow * :doc:`Detailed description of the network model and parameters ` - * :doc:`NEST implementation details `. + * :doc:`NEST implementation details ` * :doc:`code/model.py `: NEST implementation of the network model * :doc:`code/parameter_dicts.py `: parameter setting * :doc:`ignore_and_fire model documentation ` From 0073c7535f027e3db36709e6f3334a0b01819a51 Mon Sep 17 00:00:00 2001 From: Tom Tetzlaff Date: Thu, 29 Feb 2024 16:02:14 +0100 Subject: [PATCH 23/38] revision docu ignore_and_fire usecase --- pynest/examples/ignore_and_fire/README.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pynest/examples/ignore_and_fire/README.rst b/pynest/examples/ignore_and_fire/README.rst index 7670503459..0836053360 100644 --- a/pynest/examples/ignore_and_fire/README.rst +++ b/pynest/examples/ignore_and_fire/README.rst @@ -17,7 +17,7 @@ Network model In this example, we employ a simple network model describing the dynamics of a local cortical circuit at the spatial scale of ~1mm within a single cortical layer. It is derived from the model proposed in [2]_, but accounts for the synaptic weight dynamics for connections between excitatory neurons. The weight dynamics are described by the spike-timing-dependent plasticity (STDP) model derived in [8]_. The model provides a mechanism underlying the formation of broad distributions of synaptic weights in combination with asynchronous irregular spiking activity (see figure below). -A variant of this model, the :doc:`hpc_benchmark `, has been used in a number of benchmarking studies, in particular for weak-scaling experiments ([3]_, [4]_, [5]_, [6]_, [7]_). Due to its random homogeneous connectivity, the model represents a hard benchmarking scenario: each neuron projects with equal probability to any other neuron in the network. Implementations of this model can therefore not exploit any spatial connectivity patterns. In contrast to the model used here, the plasticity dynamics in the ``hpc_benchmark`` is parameterized such that it has only a weak effect on the synaptic weights and, hence, the network dynamics. Here, the effect of the synaptic plasticity is substantial and leads to a significant broadening of the weight distribution (see figure below). Synaptic weights thereby become a sensitive target metric for verification and validation studies. +A variant of this model, the :doc:`hpc_benchmark `, has been used in a number of benchmarking studies, in particular for weak-scaling experiments ([3]_-[7]_). Due to its random homogeneous connectivity, the model represents a hard benchmarking scenario: each neuron projects with equal probability to any other neuron in the network. Implementations of this model can therefore not exploit any spatial connectivity patterns. In contrast to the model used here, the plasticity dynamics in the ``hpc_benchmark`` is parameterized such that it has only a weak effect on the synaptic weights and, hence, the network dynamics. Here, the effect of the synaptic plasticity is substantial and leads to a significant broadening of the weight distribution (see figure below). Synaptic weights thereby become a sensitive target metric for verification and validation studies. Comparison between the networks with ``integrate-and-fire`` and ``ignore-and-fire`` dynamics From c8c2568de38b236f99f95d18d7680497cbdc4137 Mon Sep 17 00:00:00 2001 From: Tom Tetzlaff Date: Thu, 29 Feb 2024 16:03:54 +0100 Subject: [PATCH 24/38] revision docu ignore_and_fire usecase --- pynest/examples/ignore_and_fire/README.rst | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/pynest/examples/ignore_and_fire/README.rst b/pynest/examples/ignore_and_fire/README.rst index 0836053360..59351eb948 100644 --- a/pynest/examples/ignore_and_fire/README.rst +++ b/pynest/examples/ignore_and_fire/README.rst @@ -47,11 +47,9 @@ Scaling experiments The ``ignore_and_fire`` variant of the network model permits exact scaling experiments, without the need for any parameter tuning when changing the network size (see figure below). We demonstrate this here by simulating the network for various network sizes between :math:`N=1250` and :math:`N=13750`. The number of incoming connections per neuron, the in-degree, is kept constant at :math:`K=1250`. -The total number of connections hence scales linearly with :math:`N=1250`. For each simulation, we monitor the simulation (wall clock) time, the time and population averaged firing rate, and the mean standard deviation of the excitatory synaptic weights at the end of the simulation. +The total number of connections hence scales linearly with :math:`N`. For each simulation, we monitor the simulation (wall clock) time, the time and population averaged firing rate, and the mean standard deviation of the excitatory synaptic weights at the end of the simulation. -For the integrate-and-fire variant of the network model, the firing rate and the synaptic-weight distribution depend on the network size :math:`N`. In particular, for small :math:`N`, the firing rates and the synaptic weights increase due to the denser connectivity. - -For the ignore-and-fire version, the dynamics is independent of the network size. The average firing rate is --by definition-- constant. As the firing phases of the ignore-and-fire neurons are chosen randomly, the spiking activity is asynchronous, irrespective of the connection density. As a consequence, the distribution of synaptic weights (which is shaped by correlations between neurons) remains constant, too. The overall communication load, i.e., the total number of spikes that need to be sent across the network within each time interval, is hence fully controlled by the user. +For the integrate-and-fire variant of the network model, the firing rate and the synaptic-weight distribution depend on the network size :math:`N`. In particular, for small :math:`N`, the firing rates and the synaptic weights increase due to the denser connectivity. For the ignore-and-fire version, in contrast, the dynamics is independent of the network size. The average firing rate is --by definition-- constant. As the firing phases of the ignore-and-fire neurons are chosen randomly, the spiking activity is asynchronous, irrespective of the connection density. As a consequence, the distribution of synaptic weights (which is shaped by correlations between neurons) remains constant, too. The overall communication load, i.e., the total number of spikes that need to be sent across the network within each time interval, is hence fully controlled by the user. .. figure:: figures/scaling.png :scale: 50% From b50a60dbaa3c7402dba72bbc494bdc912f53c854 Mon Sep 17 00:00:00 2001 From: Tom Tetzlaff Date: Thu, 29 Feb 2024 17:36:06 +0100 Subject: [PATCH 25/38] revision docu ignore_and_fire usecase --- pynest/examples/ignore_and_fire/README.rst | 9 ++++++--- pynest/examples/ignore_and_fire/Snakefile | 17 +++++++++-------- 2 files changed, 15 insertions(+), 11 deletions(-) diff --git a/pynest/examples/ignore_and_fire/README.rst b/pynest/examples/ignore_and_fire/README.rst index 59351eb948..d5053c2573 100644 --- a/pynest/examples/ignore_and_fire/README.rst +++ b/pynest/examples/ignore_and_fire/README.rst @@ -17,7 +17,7 @@ Network model In this example, we employ a simple network model describing the dynamics of a local cortical circuit at the spatial scale of ~1mm within a single cortical layer. It is derived from the model proposed in [2]_, but accounts for the synaptic weight dynamics for connections between excitatory neurons. The weight dynamics are described by the spike-timing-dependent plasticity (STDP) model derived in [8]_. The model provides a mechanism underlying the formation of broad distributions of synaptic weights in combination with asynchronous irregular spiking activity (see figure below). -A variant of this model, the :doc:`hpc_benchmark `, has been used in a number of benchmarking studies, in particular for weak-scaling experiments ([3]_-[7]_). Due to its random homogeneous connectivity, the model represents a hard benchmarking scenario: each neuron projects with equal probability to any other neuron in the network. Implementations of this model can therefore not exploit any spatial connectivity patterns. In contrast to the model used here, the plasticity dynamics in the ``hpc_benchmark`` is parameterized such that it has only a weak effect on the synaptic weights and, hence, the network dynamics. Here, the effect of the synaptic plasticity is substantial and leads to a significant broadening of the weight distribution (see figure below). Synaptic weights thereby become a sensitive target metric for verification and validation studies. +A variant of this model, the :doc:`hpc_benchmark `, has been used in a number of benchmarking studies, in particular for weak-scaling experiments ([3]_, [4]_, [5]_, [6]_, [7]_). Due to its random homogeneous connectivity, the model represents a hard benchmarking scenario: each neuron projects with equal probability to any other neuron in the network. Implementations of this model can therefore not exploit any spatial connectivity patterns. In contrast to the model used here, the plasticity dynamics in the ``hpc_benchmark`` is parameterized such that it has only a weak effect on the synaptic weights and, hence, the network dynamics. Here, the effect of the synaptic plasticity is substantial and leads to a significant broadening of the weight distribution (see figure below). Synaptic weights thereby become a sensitive target metric for verification and validation studies. Comparison between the networks with ``integrate-and-fire`` and ``ignore-and-fire`` dynamics @@ -49,7 +49,10 @@ The ``ignore_and_fire`` variant of the network model permits exact scaling exper The number of incoming connections per neuron, the in-degree, is kept constant at :math:`K=1250`. The total number of connections hence scales linearly with :math:`N`. For each simulation, we monitor the simulation (wall clock) time, the time and population averaged firing rate, and the mean standard deviation of the excitatory synaptic weights at the end of the simulation. -For the integrate-and-fire variant of the network model, the firing rate and the synaptic-weight distribution depend on the network size :math:`N`. In particular, for small :math:`N`, the firing rates and the synaptic weights increase due to the denser connectivity. For the ignore-and-fire version, in contrast, the dynamics is independent of the network size. The average firing rate is --by definition-- constant. As the firing phases of the ignore-and-fire neurons are chosen randomly, the spiking activity is asynchronous, irrespective of the connection density. As a consequence, the distribution of synaptic weights (which is shaped by correlations between neurons) remains constant, too. The overall communication load, i.e., the total number of spikes that need to be sent across the network within each time interval, is hence fully controlled by the user. +For the integrate-and-fire variant of the network model, the firing rate and the synaptic-weight distribution depend on the network size :math:`N`. In particular for small :math:`N`, the firing rates and the synaptic weights increase due to the denser connectivity. For the ignore-and-fire version, in contrast, the dynamics is independent of the network size. The average firing rate is --by definition-- constant. As the firing phases of the ignore-and-fire neurons are chosen randomly, the spiking activity is asynchronous, irrespective of the connection density. As a consequence, the distribution of synaptic weights (which is shaped by cross-correlations in the spiking activity) remains constant, too. + +With the ignore-and-fire version of the model, performance benchmarking studies can thus be performed under better defined conditions. For example, the overall communication load, i.e., the total number of spikes that need to be sent across the network within each time interval, is fully controlled by the user. + .. figure:: figures/scaling.png :scale: 50% @@ -60,7 +63,7 @@ Dependence of the simulation time (top), the time and population averaged firing Running the simulations ------------------------ -The workflows underlying the numerical experiments shown above are documented in `Snakefile `. +The workflows underlying the numerical experiments shown above are documented in the `Snakefile `. References diff --git a/pynest/examples/ignore_and_fire/Snakefile b/pynest/examples/ignore_and_fire/Snakefile index d90cde1ba1..6cb907f6d4 100644 --- a/pynest/examples/ignore_and_fire/Snakefile +++ b/pynest/examples/ignore_and_fire/Snakefile @@ -1,6 +1,6 @@ rule all: """ - Creates all figures for use case documentation. + Runs simulations and creates all figures for the ignore-and-fire usecase documentation. """ input: 'figures/TwoPopulationNetworkPlastic_iaf_psc_alpha_spikes.png', @@ -15,7 +15,7 @@ rule all: rule generate_reference_data_LIF: """ - Simulate network with LIF neurons, and store spikes and synaptic weights. + Simulates network with LIF neurons, and stores spikes and synaptic weights. """ input: 'code/model.py', @@ -28,7 +28,7 @@ rule generate_reference_data_LIF: rule generate_reference_data_IgAF: """ - Simulate network with ignore-and-fire neurons, and store spikes and synaptic weights. + Simulates network with ignore-and-fire neurons, and stores spikes and synaptic weights. """ input: 'code/model.py', @@ -41,8 +41,8 @@ rule generate_reference_data_IgAF: rule generate_reference_figures_LIF: """ - Create figures showing spiking activity, weight matrices and weight distributions - before and after simulations from data obtained by simulating model + Creates figures showing spiking activity, weight matrices and weight distributions + before and after simulations from data obtained by simulating the model with LIF neurons. """ input: @@ -60,8 +60,8 @@ rule generate_reference_figures_LIF: rule generate_reference_figures_IgAF: """ - Create figures showing spiking activity, weight matrices and weight distributions - before and after simulations from data obtained by simulating model + Creates figures showing spiking activity, weight matrices and weight distributions + before and after simulations from data obtained by simulating the model with ignore_and_fire neurons. """ input: @@ -80,7 +80,8 @@ rule generate_reference_figures_IgAF: rule scaling_experiment: """ Performs scaling experiments with LIF and ignore-and-fire neurons, - and creates figure showing the results. + and creates figure showing the simulation times, average firing rates, + and synaptic weights for a range of network sizes. """ input: 'code/model.py', From f774eeab5deb65cc50b34b2b28e746162d3028cf Mon Sep 17 00:00:00 2001 From: Jessica Mitchell Date: Thu, 21 Mar 2024 09:29:54 +0100 Subject: [PATCH 26/38] minor fixes --- pynest/examples/ignore_and_fire/README.rst | 20 +++-------- pynest/examples/ignore_and_fire/Snakefile | 33 +++++++++---------- .../examples/ignore_and_fire/code/README.rst | 5 +++ 3 files changed, 25 insertions(+), 33 deletions(-) create mode 100644 pynest/examples/ignore_and_fire/code/README.rst diff --git a/pynest/examples/ignore_and_fire/README.rst b/pynest/examples/ignore_and_fire/README.rst index d5053c2573..06a89d82dd 100644 --- a/pynest/examples/ignore_and_fire/README.rst +++ b/pynest/examples/ignore_and_fire/README.rst @@ -73,7 +73,7 @@ References networks is limited by one-to-one mapping between effective connectivity and correlations. PLoS computational biology, 11(9), e1004490. - + .. [2] Brunel N (2000). Dynamics of networks of randomly connected excitatory and inhibitory spiking neurons. Journal of Physiology-Paris 94(5-6):445-463. @@ -104,7 +104,7 @@ References plasticity in balanced random networks. Neural Computation 19(6):1437-1467. `: simulation workflow @@ -114,21 +114,9 @@ References * :doc:`code/parameter_dicts.py `: parameter setting * :doc:`ignore_and_fire model documentation ` - + .. toctree:: :hidden: :glob: - ModelDescription_TwoPopulationNetworkPlastic - * - simulation_details - - - - -Indices and tables -================== - -* :ref:`genindex` -* :ref:`modindex` -* :ref:`search` + doc/* diff --git a/pynest/examples/ignore_and_fire/Snakefile b/pynest/examples/ignore_and_fire/Snakefile index 6cb907f6d4..fd23827c6e 100644 --- a/pynest/examples/ignore_and_fire/Snakefile +++ b/pynest/examples/ignore_and_fire/Snakefile @@ -6,13 +6,13 @@ rule all: 'figures/TwoPopulationNetworkPlastic_iaf_psc_alpha_spikes.png', 'figures/TwoPopulationNetworkPlastic_iaf_psc_alpha_weight_distributions.png', 'figures/TwoPopulationNetworkPlastic_iaf_psc_alpha_connectivity_presim.png', - 'figures/TwoPopulationNetworkPlastic_iaf_psc_alpha_connectivity_postsim.png', + 'figures/TwoPopulationNetworkPlastic_iaf_psc_alpha_connectivity_postsim.png', 'figures/TwoPopulationNetworkPlastic_ignore_and_fire_spikes.png', 'figures/TwoPopulationNetworkPlastic_ignore_and_fire_weight_distributions.png', 'figures/TwoPopulationNetworkPlastic_ignore_and_fire_connectivity_presim.png', - 'figures/TwoPopulationNetworkPlastic_ignore_and_fire_connectivity_postsim.png', + 'figures/TwoPopulationNetworkPlastic_ignore_and_fire_connectivity_postsim.png', 'figures/scaling.png' - + rule generate_reference_data_LIF: """ Simulates network with LIF neurons, and stores spikes and synaptic weights. @@ -20,7 +20,7 @@ rule generate_reference_data_LIF: input: 'code/model.py', 'code/parameter_dicts.py', - 'code/generate_reference_data.py' + 'code/generate_reference_data.py' output: temp('.LIF_simulation_done') run: @@ -33,15 +33,15 @@ rule generate_reference_data_IgAF: input: 'code/model.py', 'code/parameter_dicts.py', - 'code/generate_reference_data.py' + 'code/generate_reference_data.py' output: temp('.IgAF_simulation_done') run: shell('cd code; python generate_reference_data.py ignore_and_fire; cd .. ; touch {output}') - + rule generate_reference_figures_LIF: """ - Creates figures showing spiking activity, weight matrices and weight distributions + Creates figures showing spiking activity, weight matrices and weight distributions before and after simulations from data obtained by simulating the model with LIF neurons. """ @@ -49,37 +49,37 @@ rule generate_reference_figures_LIF: 'code/model.py', 'code/parameter_dicts.py', 'code/generate_reference_figures.py', - '.LIF_simulation_done' + '.LIF_simulation_done' output: 'figures/TwoPopulationNetworkPlastic_iaf_psc_alpha_spikes.png', 'figures/TwoPopulationNetworkPlastic_iaf_psc_alpha_weight_distributions.png', 'figures/TwoPopulationNetworkPlastic_iaf_psc_alpha_connectivity_presim.png', - 'figures/TwoPopulationNetworkPlastic_iaf_psc_alpha_connectivity_postsim.png', + 'figures/TwoPopulationNetworkPlastic_iaf_psc_alpha_connectivity_postsim.png', run: shell('cd code; python generate_reference_figures.py iaf_psc_alpha') rule generate_reference_figures_IgAF: """ - Creates figures showing spiking activity, weight matrices and weight distributions - before and after simulations from data obtained by simulating the model + Creates figures showing spiking activity, weight matrices and weight distributions + before and after simulations from data obtained by simulating the model with ignore_and_fire neurons. """ input: 'code/model.py', 'code/parameter_dicts.py', 'code/generate_reference_figures.py', - '.IgAF_simulation_done' + '.IgAF_simulation_done' output: 'figures/TwoPopulationNetworkPlastic_ignore_and_fire_spikes.png', 'figures/TwoPopulationNetworkPlastic_ignore_and_fire_weight_distributions.png', 'figures/TwoPopulationNetworkPlastic_ignore_and_fire_connectivity_presim.png', - 'figures/TwoPopulationNetworkPlastic_ignore_and_fire_connectivity_postsim.png', + 'figures/TwoPopulationNetworkPlastic_ignore_and_fire_connectivity_postsim.png', run: shell('cd code; python generate_reference_figures.py ignore_and_fire') rule scaling_experiment: """ - Performs scaling experiments with LIF and ignore-and-fire neurons, + Performs scaling experiments with LIF and ignore-and-fire neurons, and creates figure showing the simulation times, average firing rates, and synaptic weights for a range of network sizes. """ @@ -90,9 +90,8 @@ rule scaling_experiment: output: 'figures/scaling.png' run: - shell('cd code; python scaling.py') + shell('cd code; python scaling.py') rule clean: run: shell("rm figures/*") - shell("rm -rf code/data/*") - + shell("rm -rf code/data/*") diff --git a/pynest/examples/ignore_and_fire/code/README.rst b/pynest/examples/ignore_and_fire/code/README.rst new file mode 100644 index 0000000000..a81c545511 --- /dev/null +++ b/pynest/examples/ignore_and_fire/code/README.rst @@ -0,0 +1,5 @@ +README +====== + + +.. Do not delete required for Sphinx generation of example From a92fa201070c481eb6c6584aa1ab17db0fe7a3be Mon Sep 17 00:00:00 2001 From: Jessica Mitchell Date: Mon, 5 Aug 2024 13:36:13 +0200 Subject: [PATCH 27/38] move images to static/img remove docs on generateing svg --- ...workSketch_TwoPopulationNetworkPlastic.svg | 0 ...tic_iaf_psc_alpha_connectivity_postsim.png | Bin ...stic_iaf_psc_alpha_connectivity_presim.png | Bin ...ionNetworkPlastic_iaf_psc_alpha_spikes.png | Bin ...tic_iaf_psc_alpha_weight_distributions.png | Bin ...c_ignore_and_fire_connectivity_postsim.png | Bin ...ic_ignore_and_fire_connectivity_presim.png | Bin ...nNetworkPlastic_ignore_and_fire_spikes.png | Bin ...c_ignore_and_fire_weight_distributions.png | Bin .../static/img/scaling_ignore_and_fire.png | Bin ...escription_TwoPopulationNetworkPlastic.rst | 591 -------- ...orkSketch_TwoPopulationNetworkPlastic.fodg | 1269 ----------------- .../ignore_and_fire/doc/figures/Snakefile | 33 - .../{doc => 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a/pynest/examples/ignore_and_fire/figures/scaling.png b/doc/htmldoc/static/img/scaling_ignore_and_fire.png similarity index 100% rename from pynest/examples/ignore_and_fire/figures/scaling.png rename to doc/htmldoc/static/img/scaling_ignore_and_fire.png diff --git a/pynest/examples/ignore_and_fire/doc/ModelDescription_TwoPopulationNetworkPlastic.rst b/pynest/examples/ignore_and_fire/doc/ModelDescription_TwoPopulationNetworkPlastic.rst deleted file mode 100644 index 9fbedb7e40..0000000000 --- a/pynest/examples/ignore_and_fire/doc/ModelDescription_TwoPopulationNetworkPlastic.rst +++ /dev/null @@ -1,591 +0,0 @@ -.. _sec_model_description: - -Two population STDP network model description -============================================= - -Summary of network model ------------------------- - -.. list-table:: - :stub-columns: 1 - - * - **Populations** - - excitatory population :math:`\mathcal{E}`, inhibitory population :math:`\mathcal{I}`, external Poissonian spike sources :math:`\mathcal{X}` - * - **Connectivity** - - sparse random connectivity respecting Dale’s principle - * - **Neurons** - - leaky integrate-and-fire (LIF) - * - **Synapses** - - linear input integration with alpha-function-shaped postsynaptic currents (PSCs), spike-timing dependent plasticity (STDP) for connections between excitatory neurons - * - **Input** - - stationary, uncorrelated Poissonian spike trains - - * - - - .. figure:: figures/NetworkSketch_TwoPopulationNetworkPlastic.svg - - Network sketch (see `Fig. 8 `_ - in Senk et al. [1]_). - -Detailed desciption of network model ------------------------------------- - -Populations -~~~~~~~~~~~ - -.. table:: - - +---------------------+----------------------+----------------------------------+ - | **Name** | **Elements** | **Size** | - +=====================+======================+==================================+ - | :math:`\mathcal{E}` | LIF neurons | :math:`N_\text{E}=\beta{}N` | - | | | | - +---------------------+----------------------+----------------------------------+ - | :math:`\mathcal{I}` | LIF neurons | :math:`N_\text{I}=N-N_\text{E}` | - | | | | - +---------------------+----------------------+----------------------------------+ - | :math:`\mathcal{X}` | realizations of a | :math:`N` | - | | Poisson point | | - | | process | | - +---------------------+----------------------+----------------------------------+ - -Connectivity -~~~~~~~~~~~~ - -.. table:: - - +---------------------+----------------------+---------------------------------------------------------+ - | **Source** | **Target** | **Pattern** | - +=====================+======================+=========================================================+ - | :math:`\mathcal{E}` | :math:`\mathcal{E}` | - random, | - | | | independent; | - | | | homogeneous | - | | | in-degree | - | | | :math:`K_{\text{E},i}=K_\text{E}` | - | | | | - | | | (:math:`\forall{}i\in\mathcal{E}`) | - | | | | - | | | | - | | | - plastic synaptic weights | - | | | :math:`J_{ij}(t)` | - | | | | - | | | (:math:`\forall{}i\in\mathcal{E},j\in\mathcal{E}`) | - | | | | - | | | - homogeneous | - | | | | - | | | spike-transmission | - | | | delays | - | | | :math:`d_{ij}=d` | - | | | | - | | | (:math:`\forall{}i\in\mathcal{E},j\in\mathcal{E}`) | - | | | | - +---------------------+----------------------+---------------------------------------------------------+ - | :math:`\mathcal{E}` | :math:`\mathcal{I}` | - random, | - | | | independent; | - | | | homogeneous | - | | | in-degree | - | | | :math:`K_{\text{E},i}=K_\text{E}` | - | | | | - | | | (:math:`\forall{}i\in\mathcal{I}`) | - | | | | - | | | | - | | | - fixed synaptic | - | | | weights | - | | | :math:`J_{ij}\in\{0,J\}` | - | | | | - | | | (:math:`\forall{}i\in\mathcal{I},j\in\mathcal{E}`) | - | | | | - | | | | - | | | | - | | | - homogeneous | - | | | | - | | | spike-transmission | - | | | delays | - | | | :math:`d_{ij}=d` | - | | | | - | | | (:math:`\forall{}i\in\mathcal{I},j\in\mathcal{E}`) | - | | | | - +---------------------+----------------------+---------------------------------------------------------+ - | :math:`\mathcal{I}` | :math:`\mathcal \ | - random, | - | | {E}\cup\mathcal{I}` | independent; | - | | | homogeneous | - | | | in-degree | - | | | :math:`K_{\text{I},i}=K_\text{I}` | - | | | | - | | | (:math:`forall{}i\in\mathcal{E}\cup\mathcal{I}`)\ | - | | | j\in\mathcal{I}`) | - | | | | - | | | - fixed synaptic | - | | | weights | - | | | :math:`J_{ij}\in\{-gJ,0\}` | - | | | | - | | | (:math:`\forall{}i\in\mathcal{E}\cup\mathcal{I}, \ | - | | | j\in\mathcal{I}`) | - | | | | - | | | | - | | | - homogeneous | - | | | | - | | | spike-transmission | - | | | delays | - | | | :math:`d_{ij}=d` | - | | | | - | | | (:math:`\forall{}i\in\mathcal{E}\cup\mathcal{I}, \ | - | | | j\in\mathcal{I}`) | - | | | | - | | | | - +---------------------+----------------------+---------------------------------------------------------+ - | :math:`\mathcal{X}` | :math:`\mathcal \ | - one-to-one | - | | {E}\cup\mathcal{I}` | | - | | | - fixed synaptic | - | | | weights | - | | | :math:`J_{ij}=J` | - | | | | - | | | (:math:`\forall{}i\in\mathcal{E}\cup\mathcal{I}, \ | - | | | j\in\mathcal{X}`) | - | | | | - | | | - homogeneous | - | | | | - | | | spike-transmission | - | | | delays | - | | | :math:`d_{ij}=d` | - | | | | - | | | (:math:`\forall{}i\in\mathcal{E}\cup\mathcal{I}, \ | - | | | j\in\mathcal{X}`) | - | | | | - +---------------------+----------------------+---------------------------------------------------------+ - - - -Neuron -~~~~~~ - -.. list-table:: - - * - **Leaky integrate-and-fire (iaf) dynamics** - - Dynamics of membrane potential :math:`V_{i}(t)` and - spiking activity :math:`s_i(t)` of neuro :math:`i\in\left\{1,\ldots,N\right\}`: - - * emission of :math:`k`\ th (:math:`k=1,2,\ldots`) spike of neuron - :math:`i` at time :math:`t_{i}^{k}` if - - .. math:: - V_{i}\left(t_{i}^{k}\right)\geq\theta - - with spike threshold :math:`\theta` - - * reset and refractoriness: - - .. math:: \forall{}k,\ \forall t \in \left(t_{k}^{i},\,t_{k}^{i}+\tau_\text{ref}\right]:\quad V_{i}(t)=V_\text{reset} - - with refractory period :math:`\tau_\text{ref}` and reset potential - :math:`V_\text{reset}` - - * spike train :math:`\displaystyle s_i(t)=\sum_k \delta(t-t_i^k)` - - * subthreshold dynamics of membrane potential :math:`V_{i}(t)`: - - .. math:: - - \begin{aligned} - &\forall{}k,\ \forall t \notin \left[t_{i}^{k},\,t_{i}^{k}+\tau_\text{ref}\right):\\ - &\qquad\tau_\text{m}\frac{\text{d}{}V_i(t)}{\text{d}{}t} = - \Bigl[E_\text{L}-V_i(t)\Bigr]+R_\text{m}I_i(t) - \end{aligned} - - with membrane time constant :math:`\tau_\text{m}`, membrane - resistance :math:`R_\text{m}`, resting potential :math:`E_\text{L}`, - and total synaptic input current :math:`I_i(t)` - - -Synapse: transmission -~~~~~~~~~~~~~~~~~~~~~ - -.. list-table:: - - * - **Current-based synapses with alpha-function shaped postsynaptic currents (PSCs)** - - - - Total synaptic input current of neuron :math:`i` - - .. math:: I_i(t)=I_{\text{E},i}(t)+I_{\text{I},i}(t)+I_{\text{X},i}(t) - - * excitatory, inhibitory and external synaptic input currents - - .. math:: - - %I_{P,i}(t)=\sum_{j\in\mathcal{P}}(\text{PSC}_{ij}*s_j)(t) - %\quad\text{for}\quad - %(P,\mathcal{P})\in\{(\exc,\Epop),(\inh,\Ipop),(\ext,\Xpop)\} - %, - \begin{aligned} - I_{\text{E},i}(t)&=\sum_{j\in\mathcal{E}}\bigl(\text{PSC}_{ij}*s_j\bigr)(t-d_{ij})\\ - I_{\text{I},i}(t)&=\sum_{j\in\mathcal{I}}\bigl(\text{PSC}_{ij}*s_j\bigr)(t-d_{ij})\\ - I_{\text{X},i}(t)&=\sum_{j\in\mathcal{X}}\bigl(\text{PSC}_{ij}*s_j\bigr)(t-d_{ij}) - \end{aligned} - - with spike trains :math:`s_j(t)` of local - (:math:`j\in\mathcal{E}\cup\mathcal{I}`) and external sources - (:math:`j\in\mathcal{X}`), spike transmission delays :math:`d_{ij}`, - and convolution operator “:math:`*`”: - :math:`\displaystyle\bigl(f*g\bigr)(t)=\int_{-\infty}^\infty\text{d}s\,f(s)g(t-s)`) - - * alpha-function shaped postsynaptic currents - - .. math:: \text{PSC}_{ij}(t)=\hat{I}_{ij}e\tau_\text{s}^{-1}te^{-t/\tau_\text{s}}\Theta(t) - - with synaptic time constant :math:`\tau_\text{s}` and Heaviside - function :math:`\Theta(\cdot)` - - * postsynaptic potential triggered by a single presynaptic spike - - .. math:: - - \text{PSP}_{ij}(t)= - \hat{I}_{ij}\frac{e}{\tau_\text{s}C_\text{m}} - \left(\frac{1}{\tau_\text{m}}-\frac{1}{\tau_\text{s}}\right)^{-2} - \left(\left(\frac{1}{\tau_\text{m}}-\frac{1}{\tau_\text{s}}\right) t e^{-t/\tau_\text{s}} - e^{-t/\tau_\text{s}} + e^{-t/\tau_\text{m}} \right) \Theta(t) - - * PSC amplitude (synaptic weight) - - .. math:: - - \hat{I}_{ij}=\text{max}_t\bigl(\text{PSC}_{ij}(t)\bigr) - =\frac{J_{ij}}{J_\text{unit}(\tau_\text{m},\tau_\text{s},C_\text{m})} - - parameterized by PSP amplitude - :math:`J_{ij}=\text{max}_t\bigl(\text{PSP}_{ij}(t)\bigr)` - - with unit PSP amplitude (PSP amplitude for :math:`\hat{I}_{ij}=1`): - - .. math:: - - J_\text{unit}(\tau_\text{m},\tau_\text{s},C_\text{m}) - = \frac{e}{C_\text{m}\left(1-\frac{\tau_\text{s}}{\tau_\text{m}}\right)}\left( \frac{e^{-t_\text{max}/\tau_\text{m}} - e^{-t_\text{max}/\tau_\text{s}}}{\frac{1}{\tau_\text{s}} - \frac{1}{\tau_\text{m}}} - t_\text{max}e^{-t_\text{max}/\tau_\text{s}} \right), - - time to PSP maximum - - .. math:: - - t_\text{max} = - \frac{1}{\frac{1}{\tau_\text{s}} - \frac{1}{\tau_\text{m}}}\left(-W_{-1}\left(\frac{-\tau_\text{s}e^{-\frac{\tau_\text{s}}{\tau_\text{m}}}}{\tau_\text{m}}\right) - \frac{\tau_\text{s}}{\tau_\text{m}}\right), - - and Lambert-W function :math:`\displaystyle W_{-1}(x)` for - :math:`\displaystyle x \ge -1/e` - - - -Synapse: plasticity -~~~~~~~~~~~~~~~~~~~ - -.. list-table:: - - * - **Spike-timing dependent plasticity (STDP) with power-law weight dependence and all-to-all spike pairing scheme.** - See Morrison et al. [2]_ for connections between excitatory neurons. - - - - Dynamics of synaptic weights :math:`J_{ij}(t)` :math:`\forall{}i\in\mathcal{E}, j\in\mathcal{E}`: - - .. math:: - - \begin{aligned} - &\forall J_{ij}\ge{}0: \\[1ex] - &\quad - \frac{\text{d}}{}J_{ij}{\text{d}{}t}= - \lambda^+f^+(J_{ij})\sum_k x^+_j(t)\delta\Bigl(t-[t_i^k+d_{ij}]\Bigr) - + \lambda^-f^-(J_{ij})\sum_l x^-_i(t)\delta\Big(t-[t_j^l-d_{ij}]\Bigr)\\[1ex] - &\forall{}\{t|J_{ij}(t)<0\}: \quad J_{ij}(t)=0 \quad \text{(clipping)} - \end{aligned} - - with - - - pre- and postsynaptic spike times :math:`\{t_j^l|l=1,2,\ldots\}` and - :math:`\{t_i^k|k=1,2,\ldots\}`, - - - magnitude :math:`\lambda^+=\lambda` of weight update for causal - firing (postsynaptic spike following presynaptic spikes: - :math:`t_i^k>t_j^l`), - - - magnitude :math:`\lambda^-=-\alpha\lambda` of weight update for - acausal firing (presynaptic spike following postsynaptic spikes: - :math:`t_i^k - - - 2023-07-04T10:55:29.1428065672023-08-22T10:41:05.856932532PT1H46M41S13LibreOffice/7.3.7.2$Linux_X86_64 LibreOffice_project/30$Build-2 - 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- output: - 'NetworkSketch_TwoPopulationNetworkPlastic.svg' - - run: - shell('inkscape --export-type=svg {input}') - -rule clean: - run: - shell("rm *.pdf *.svg") diff --git a/pynest/examples/ignore_and_fire/doc/simulation_details.rst b/pynest/examples/ignore_and_fire/simulation_details.rst similarity index 95% rename from pynest/examples/ignore_and_fire/doc/simulation_details.rst rename to pynest/examples/ignore_and_fire/simulation_details.rst index d1607fbaa7..9cb166d5d3 100644 --- a/pynest/examples/ignore_and_fire/doc/simulation_details.rst +++ b/pynest/examples/ignore_and_fire/simulation_details.rst @@ -1,8 +1,5 @@ -Two population STDP network model simulation -============================================ - Simulation details ------------------- +================== By default, this implementation is based on the LIF neuron, :doc:`iaf_psc_alpha ` and the From 90c84f447dc658e6187b3b19d7e301c981e1fbd5 Mon Sep 17 00:00:00 2001 From: Jessica Mitchell Date: Mon, 5 Aug 2024 13:38:24 +0200 Subject: [PATCH 28/38] rename py files --- ...enerate_reference_data-ignore_and_fire.py} | 35 ++++-- ...rate_reference_figures-ignore_and_fire.py} | 105 ++++++++++-------- .../model.py => model-ignore_and_fire.py} | 63 ++++++----- ...s.py => parameter_dict-ignore_and_fire.py} | 21 +++- .../scaling.py => scaling-ignore_and_fire.py} | 76 ++++++++----- 5 files changed, 190 insertions(+), 110 deletions(-) rename pynest/examples/ignore_and_fire/{code/generate_reference_data.py => generate_reference_data-ignore_and_fire.py} (60%) rename pynest/examples/ignore_and_fire/{code/generate_reference_figures.py => generate_reference_figures-ignore_and_fire.py} (68%) rename pynest/examples/ignore_and_fire/{code/model.py => model-ignore_and_fire.py} (94%) rename pynest/examples/ignore_and_fire/{code/parameter_dicts.py => parameter_dict-ignore_and_fire.py} (81%) rename pynest/examples/ignore_and_fire/{code/scaling.py => scaling-ignore_and_fire.py} (83%) diff --git a/pynest/examples/ignore_and_fire/code/generate_reference_data.py b/pynest/examples/ignore_and_fire/generate_reference_data-ignore_and_fire.py similarity index 60% rename from pynest/examples/ignore_and_fire/code/generate_reference_data.py rename to pynest/examples/ignore_and_fire/generate_reference_data-ignore_and_fire.py index 7882c73b9b..31762dd6c6 100644 --- a/pynest/examples/ignore_and_fire/code/generate_reference_data.py +++ b/pynest/examples/ignore_and_fire/generate_reference_data-ignore_and_fire.py @@ -1,14 +1,33 @@ +# +# generate_reference_data-ignore_and_fire.py +# +# This file is part of NEST. +# +# Copyright (C) 2004 The NEST Initiative +# +# NEST is free software: you can redistribute it and/or modify +# it under the terms of the GNU General Public License as published by +# the Free Software Foundation, either version 2 of the License, or +# (at your option) any later version. +# +# NEST is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU General Public License for more details. +# +# You should have received a copy of the GNU General Public License +# along with NEST. If not, see . """ Generate reference data -======================= +----------------------- """ import sys + import model import psutil -################################################# -def generate_reference_data(neuron_model='ignore_and_fire'): +def generate_reference_data(neuron_model="ignore_and_fire"): """ Generate set of reference data and store on disk (spike data and model paramaters). @@ -27,14 +46,14 @@ def generate_reference_data(neuron_model='ignore_and_fire'): parameters = model.get_default_parameters() parameters["neuron_model"] = neuron_model - + parameters["record_spikes"] = True # parameters["record_weights"] = True model_instance = model.Model(parameters) - print("\nneuron model: %s" % model_instance.pars['neuron_model']) - + print("\nneuron model: %s" % model_instance.pars["neuron_model"]) + model_instance.create() model_instance.connect() @@ -60,6 +79,4 @@ def generate_reference_data(neuron_model='ignore_and_fire'): return -################################################# - -generate_reference_data(neuron_model = sys.argv[1]) +generate_reference_data(neuron_model=sys.argv[1]) diff --git a/pynest/examples/ignore_and_fire/code/generate_reference_figures.py b/pynest/examples/ignore_and_fire/generate_reference_figures-ignore_and_fire.py similarity index 68% rename from pynest/examples/ignore_and_fire/code/generate_reference_figures.py rename to pynest/examples/ignore_and_fire/generate_reference_figures-ignore_and_fire.py index 322e5ea204..408029c008 100644 --- a/pynest/examples/ignore_and_fire/code/generate_reference_figures.py +++ b/pynest/examples/ignore_and_fire/generate_reference_figures-ignore_and_fire.py @@ -1,11 +1,29 @@ +# +# generate_references_figures-ignore_and_fire.py +# +# This file is part of NEST. +# +# Copyright (C) 2004 The NEST Initiative +# +# NEST is free software: you can redistribute it and/or modify +# it under the terms of the GNU General Public License as published by +# the Free Software Foundation, either version 2 of the License, or +# (at your option) any later version. +# +# NEST is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU General Public License for more details. +# +# You should have received a copy of the GNU General Public License +# along with NEST. If not, see . """ Generate reference figures -========================== +-------------------------- """ -import sys - -sys.path.append(r"../") +# sys.path.append(r"../") import os +import sys import matplotlib.pyplot as plt import model @@ -13,7 +31,7 @@ import numpy as np from matplotlib import gridspec -############################################## + def time_and_population_averaged_spike_rate(spikes, time_interval, pop_size): D = time_interval[1] - time_interval[0] n_events = sum((spikes[:, 1] >= time_interval[0]) * (spikes[:, 1] <= time_interval[1])) @@ -22,7 +40,6 @@ def time_and_population_averaged_spike_rate(spikes, time_interval, pop_size): return rate -################################################# def plot_spikes(spikes, nodes, pars, path="./"): """ Create raster plot of spiking activity. @@ -33,7 +50,7 @@ def plot_spikes(spikes, nodes, pars, path="./"): rate = time_and_population_averaged_spike_rate(spikes, (0.0, pars["T"]), pars["N_rec_spikes"]) # plot spiking activity - plt.figure(num=1) + plt.figure(num=1) plt.clf() plt.title(r"time and population averaged firing rate: $\nu=%.2f$ spikes/s" % rate) plt.plot(spikes[:, 1], spikes[:, 0], "o", ms=1, lw=0, mfc="k", mec="k", mew=0, alpha=0.2, rasterized=True) @@ -43,8 +60,8 @@ def plot_spikes(spikes, nodes, pars, path="./"): plt.xlabel(r"time (ms)") plt.ylabel(r"neuron id") - plt.subplots_adjust(bottom=0.14,top=0.9,left=0.15,right=0.95) - plt.savefig(path + "/TwoPopulationNetworkPlastic_%s_spikes.png" % pars['neuron_model']) + plt.subplots_adjust(bottom=0.14, top=0.9, left=0.15, right=0.95) + plt.savefig(path + "/TwoPopulationNetworkPlastic_%s_spikes.png" % pars["neuron_model"]) # def center_axes_ticks(ax): @@ -62,7 +79,6 @@ def plot_spikes(spikes, nodes, pars, path="./"): # ax.set_yticklabels(yticks.astype("int")) -################################################# def plot_connectivity_matrix(W, pop_pre, pop_post, pars, filename_label=None, path="./"): """ Plot connectivity matrix "W" for source and target neurons contained in "pop_pre" and "pop_post", @@ -74,7 +90,7 @@ def plot_connectivity_matrix(W, pop_pre, pop_post, pars, filename_label=None, pa print("\nPlotting connectivity matrix...") - fig = plt.figure(num=2) + fig = plt.figure(num=2) plt.clf() gs = gridspec.GridSpec(1, 2, width_ratios=[15, 1]) @@ -100,9 +116,9 @@ def plot_connectivity_matrix(W, pop_pre, pop_post, pars, filename_label=None, pa ### plt.subplots_adjust(left=0.12, bottom=0.1, right=0.9, top=0.95, wspace=0.1) - plt.savefig(path + "/TwoPopulationNetworkPlastic_%s_connectivity%s.png" % (pars['neuron_model'],filename_label)) + plt.savefig(path + "/TwoPopulationNetworkPlastic_%s_connectivity%s.png" % (pars["neuron_model"], filename_label)) + -################################################# def plot_weight_distributions(whist_presim, whist_postsim, weights, pars, path="./"): """ Plot distributions of synaptic weights before and after simulation. @@ -122,19 +138,19 @@ def plot_weight_distributions(whist_presim, whist_postsim, weights, pars, path=" plt.ylabel(r"rel. frequency") plt.xlim(weights[0], weights[-2]) plt.ylim(5e-5, 3) - #plt.subplots_adjust(left=0.12, bottom=0.12, right=0.95, top=0.95) - plt.subplots_adjust(bottom=0.14,top=0.9,left=0.15,right=0.95) - plt.savefig(path + "/TwoPopulationNetworkPlastic_%s_weight_distributions.png" % pars['neuron_model']) + # plt.subplots_adjust(left=0.12, bottom=0.12, right=0.95, top=0.95) + plt.subplots_adjust(bottom=0.14, top=0.9, left=0.15, right=0.95) + plt.savefig(path + "/TwoPopulationNetworkPlastic_%s_weight_distributions.png" % pars["neuron_model"]) + -################################################# -def generate_reference_figures(neuron_model='ignore_and_fire'): +def generate_reference_figures(neuron_model="ignore_and_fire"): """Generate and store set of reference data""" ## raster plot parameters = model.get_default_parameters() parameters["neuron_model"] = neuron_model - + parameters["record_spikes"] = True parameters["record_weights"] = True @@ -150,16 +166,12 @@ def generate_reference_figures(neuron_model='ignore_and_fire'): model_instance.pars["data_path"], "spikes-%d" % (np.array(model_instance.nodes["spike_recorder"])[0]), ) - #plot_spikes(spikes, model_instance.nodes, model_instance.pars, model_instance.pars["data_path"]) - plot_spikes(spikes, model_instance.nodes, model_instance.pars, "../figures") + # plot_spikes(spikes, model_instance.nodes, model_instance.pars, model_instance.pars["data_path"]) + plot_spikes(spikes, model_instance.nodes, model_instance.pars, "figures") ## load connectivity from reference data - connectivity_presim = model.load_connectivity_data( - model_instance.pars["data_path"], "connectivity_presim" - ) - connectivity_postsim = model.load_connectivity_data( - model_instance.pars["data_path"], "connectivity_postsim" - ) + connectivity_presim = model.load_connectivity_data(model_instance.pars["data_path"], "connectivity_presim") + connectivity_postsim = model.load_connectivity_data(model_instance.pars["data_path"], "connectivity_postsim") ## create connectivity matrices before and after simulation for a subset of neurons subset_size = 100 @@ -169,10 +181,10 @@ def generate_reference_figures(neuron_model='ignore_and_fire'): W_postsim, pop_pre, pop_post = model.get_connectivity_matrix(connectivity_postsim, pop_pre, pop_post) ## plot connectivity matrices - #plot_connectivity_matrix(W_presim, pop_pre, pop_post, "_presim", model_instance.pars["data_path"]) - #plot_connectivity_matrix(W_postsim, pop_pre, pop_post, "_postsim", model_instance.pars["data_path"]) - plot_connectivity_matrix(W_presim, pop_pre, pop_post, model_instance.pars, "_presim", "../figures") - plot_connectivity_matrix(W_postsim, pop_pre, pop_post, model_instance.pars, "_postsim", "../figures") + # plot_connectivity_matrix(W_presim, pop_pre, pop_post, "_presim", model_instance.pars["data_path"]) + # plot_connectivity_matrix(W_postsim, pop_pre, pop_post, "_postsim", model_instance.pars["data_path"]) + plot_connectivity_matrix(W_presim, pop_pre, pop_post, model_instance.pars, "_presim", "figures") + plot_connectivity_matrix(W_postsim, pop_pre, pop_post, model_instance.pars, "_postsim", "figures") ## compute weight distributions # weights = np.arange(29.5,34.1,0.05) @@ -181,22 +193,21 @@ def generate_reference_figures(neuron_model='ignore_and_fire'): whist_postsim = model.get_weight_distribution(connectivity_postsim, weights) ## plot weight distributions - #plot_weight_distributions(whist_presim, whist_postsim, weights, model_instance.pars["data_path"]) - plot_weight_distributions(whist_presim, whist_postsim, weights, model_instance.pars, "../figures") + # plot_weight_distributions(whist_presim, whist_postsim, weights, model_instance.pars["data_path"]) + plot_weight_distributions(whist_presim, whist_postsim, weights, model_instance.pars, "figures") -################################################# - from matplotlib import rcParams -rcParams['figure.figsize'] = (4,3) -rcParams['figure.dpi'] = 300 -rcParams['font.family'] = 'sans-serif' -rcParams['font.size'] = 8 -rcParams['legend.fontsize'] = 8 -rcParams['axes.titlesize'] = 8 -rcParams['axes.labelsize'] = 8 -rcParams['ytick.labelsize'] = 8 -rcParams['xtick.labelsize'] = 8 -rcParams['text.usetex'] = True - -generate_reference_figures(neuron_model = sys.argv[1]) + +rcParams["figure.figsize"] = (4, 3) +rcParams["figure.dpi"] = 300 +rcParams["font.family"] = "sans-serif" +rcParams["font.size"] = 8 +rcParams["legend.fontsize"] = 8 +rcParams["axes.titlesize"] = 8 +rcParams["axes.labelsize"] = 8 +rcParams["ytick.labelsize"] = 8 +rcParams["xtick.labelsize"] = 8 +rcParams["text.usetex"] = True + +generate_reference_figures(neuron_model=sys.argv[1]) diff --git a/pynest/examples/ignore_and_fire/code/model.py b/pynest/examples/ignore_and_fire/model-ignore_and_fire.py similarity index 94% rename from pynest/examples/ignore_and_fire/code/model.py rename to pynest/examples/ignore_and_fire/model-ignore_and_fire.py index 6021e51e53..0708fb5954 100644 --- a/pynest/examples/ignore_and_fire/code/model.py +++ b/pynest/examples/ignore_and_fire/model-ignore_and_fire.py @@ -1,3 +1,22 @@ +# +# model-ignore_and_fire.py +# +# This file is part of NEST. +# +# Copyright (C) 2004 The NEST Initiative +# +# NEST is free software: you can redistribute it and/or modify +# it under the terms of the GNU General Public License as published by +# the Free Software Foundation, either version 2 of the License, or +# (at your option) any later version. +# +# NEST is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU General Public License for more details. +# +# You should have received a copy of the GNU General Public License +# along with NEST. If not, see . """ Scalable two population STDP network model ------------------------------------------ @@ -8,7 +27,7 @@ (Brunel [1]_), but accounts for the synaptic weight dynamics for connections between excitatory neurons. The weight dynamics are described by the spike-timing-dependent plasticity (STDP) model derived -by Morrison et al. ( [7]_). The model provides a mechanism underlying the +by Morrison et al. ( [2]_). The model provides a mechanism underlying the formation of broad distributions of synaptic weights in combination with asynchronous irregular spiking activity (see figure below). A detailed @@ -26,6 +45,17 @@ Default parameters are provided in "parameter_dicts.py". For more information see :doc:`/auto_examples/ignore_and_fire/index`. +References +~~~~~~~~~~ + +.. [1] Brunel N (2000). Dynamics of networks of randomly connected excitatory + and inhibitory spiking neurons. Journal of Physiology-Paris + 94(5-6):445-463. + +.. [2] Morrison A. Aertsen, A. and Diesmann M. 2007. + Spike-timing-dependent plasticity in balanced random networks. + Neural Computation. 19(6):1437–1467. + """ import copy @@ -36,9 +66,9 @@ import numpy as np import scipy.special -############################################## -# Instantiation of the TwoPopulationNetworkPlastic model and its PyNEST implementation. -# +############################################################################### +# Instantiation of the TwoPopulationNetworkPlastic model +# and its PyNEST implementation. class Model: @@ -75,7 +105,7 @@ def __init__(self, parameters): # create data directory (if necessary) os.system("mkdir -p " + self.pars["data_path"]) - + # initialize NEST kernel nest.ResetKernel() nest.SetKernelStatus( @@ -112,7 +142,6 @@ def __init__(self, parameters): elif self.pars["neuron_model"] == "ignore_and_fire": self.__neuron_params = {} - ############################################## def __derived_parameters(self, parameters): """ Set additional parameters derived from base parameters. @@ -154,10 +183,9 @@ def __derived_parameters(self, parameters): # number of neurons spikes are recorded from if self.pars["N_rec_spikes"] == "all": self.pars["N_rec_spikes"] = self.pars["N"] - + return - ############################################## def create(self): """ Create and configure all network nodes (neurons + recording and stimulus devices), @@ -238,7 +266,6 @@ def create(self): return - ############################################## def connect(self): """ Connect network and devices. @@ -313,7 +340,6 @@ def connect(self): return - ############################################## def simulate(self, t_sim): """ Run simulation. @@ -331,7 +357,6 @@ def simulate(self, t_sim): return - ############################################## def save_parameters(self, filename_root, path): """ Save model-instance parameters to file. @@ -352,7 +377,6 @@ def save_parameters(self, filename_root, path): return - ############################################## def get_connectivity(self, pop_pre, pop_post, filename=None): """ Extract connectivity for subpopulations pop_pre and pop_post @@ -400,7 +424,6 @@ def get_connectivity(self, pop_pre, pop_post, filename=None): return C -############################################## def get_default_parameters(): """ Import default model-parameter file. @@ -419,7 +442,6 @@ def get_default_parameters(): return pars -############################################## def get_data_file_list(path, label): """ Searches for files with extension "*.dat" in directory "path" with names starting with "label", @@ -453,8 +475,6 @@ def get_data_file_list(path, label): return files -############################################## -# def load_spike_data(path, label, skip_rows = 3): def load_spike_data(path, label, time_interval=None, pop=None, skip_rows=3): """ Load spike data from files. @@ -537,7 +557,6 @@ def load_spike_data(path, label, time_interval=None, pop=None, skip_rows=3): return spikes -############################################## def load_connectivity_data(path, label, skip_rows=1): """ Load connectivity data (weights and delays) from files. @@ -586,9 +605,6 @@ def load_connectivity_data(path, label, skip_rows=1): return C -############################################# - - def unit_psp_amplitude(tau_m, C_m, tau_s): """ Compute PSP maximum (mV) for LIF with alpha-shaped PSCs with unit amplitude 1. @@ -628,14 +644,12 @@ def unit_psp_amplitude(tau_m, C_m, tau_s): return J_unit -############################################## def LambertWm1(x): y = scipy.special.lambertw(x, k=-1 if x < 0 else 0).real return y -############################################## def get_index(x, y): """ Return indices of x where x==y. @@ -655,7 +669,6 @@ def get_index(x, y): return np.where(x == y)[0] -############################################## def get_connectivity_matrix(connectivity, pop_pre=[], pop_post=[]): """ Generate connectivity matrix from connectivity data in "connectivity" @@ -705,9 +718,5 @@ def get_connectivity_matrix(connectivity, pop_pre=[], pop_post=[]): return W, pop_pre, pop_post -############################################## def get_weight_distribution(connectivity, weights): return np.histogram(connectivity[:, 2], weights, density=True)[0] - - -############################################## diff --git a/pynest/examples/ignore_and_fire/code/parameter_dicts.py b/pynest/examples/ignore_and_fire/parameter_dict-ignore_and_fire.py similarity index 81% rename from pynest/examples/ignore_and_fire/code/parameter_dicts.py rename to pynest/examples/ignore_and_fire/parameter_dict-ignore_and_fire.py index 5b4b574c00..6650ba5a47 100644 --- a/pynest/examples/ignore_and_fire/code/parameter_dicts.py +++ b/pynest/examples/ignore_and_fire/parameter_dict-ignore_and_fire.py @@ -1,6 +1,25 @@ +# +# parameter_dict-ignore_and_fire.py +# +# This file is part of NEST. +# +# Copyright (C) 2004 The NEST Initiative +# +# NEST is free software: you can redistribute it and/or modify +# it under the terms of the GNU General Public License as published by +# the Free Software Foundation, either version 2 of the License, or +# (at your option) any later version. +# +# NEST is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU General Public License for more details. +# +# You should have received a copy of the GNU General Public License +# along with NEST. If not, see . """ Parameter dictionary -==================== +-------------------- Default parameters for Two population STDP network model (TwoPopulationNetworkPlastic) diff --git a/pynest/examples/ignore_and_fire/code/scaling.py b/pynest/examples/ignore_and_fire/scaling-ignore_and_fire.py similarity index 83% rename from pynest/examples/ignore_and_fire/code/scaling.py rename to pynest/examples/ignore_and_fire/scaling-ignore_and_fire.py index 2e455d9f03..744ca5044e 100644 --- a/pynest/examples/ignore_and_fire/code/scaling.py +++ b/pynest/examples/ignore_and_fire/scaling-ignore_and_fire.py @@ -1,18 +1,36 @@ +# +# scaling-ignore_and_fire.py +# +# This file is part of NEST. +# +# Copyright (C) 2004 The NEST Initiative +# +# NEST is free software: you can redistribute it and/or modify +# it under the terms of the GNU General Public License as published by +# the Free Software Foundation, either version 2 of the License, or +# (at your option) any later version. +# +# NEST is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU General Public License for more details. +# +# You should have received a copy of the GNU General Public License +# along with NEST. If not, see . """ Scaling experiment -================== +------------------ * demonstrates scaling experiments with a plastic two-population network, * illustrates problems arising with standard ``integrate-and-fire`` dynamics, and * describes how to perform exact scaling experiments by using ``ignore_and_fire`` neurons. """ +import json import os import sys import time -import json - import matplotlib.pyplot as plt import model import numpy @@ -21,12 +39,12 @@ ########################################################## ## parameters neuron_models = ["iaf_psc_alpha", "ignore_and_fire"] ## neuron models -Ns = numpy.arange(1250, 15000, 1250) ## network sizes +Ns = numpy.arange(1250, 15000, 1250) ## network sizes -data_path_root = "./data" ## root of path to simulation data +data_path_root = "./data/" ## root of path to simulation data simulate = True -simulate = False +# simulate = False analyse = True # analyse = False @@ -51,7 +69,7 @@ def run_model(neuron_model, N, data_path_root): ## create and simulate network model_instance = model.Model(parameters) - start = time.time() + start = time.time() model_instance.create() model_instance.connect() stop = time.time() @@ -169,7 +187,6 @@ def save_dict_as_json(data_dict, filename_root, path): return -########################################################## def load_data(neuron_model, N, data_path_root): """ Loads a data dictionary from a json file. @@ -177,15 +194,13 @@ def load_data(neuron_model, N, data_path_root): data = {} ## read data from json file - data_path = data_path_root + "/N" + str(N) + "/" + neuron_model + data_path = data_path_root + "/N" + str(N) + "/" + neuron_model with open(data_path + "/data.json") as f: data = json.load(f) return data -########################################################## - if analyse: build_time = numpy.zeros((len(neuron_models), len(Ns))) sim_time = numpy.zeros_like(build_time) @@ -224,16 +239,17 @@ def load_data(neuron_model, N, data_path_root): ## plotting from matplotlib import rcParams - rcParams['figure.figsize'] = (3,4) - rcParams['figure.dpi'] = 300 - rcParams['font.family'] = 'sans-serif' - rcParams['font.size'] = 8 - rcParams['legend.fontsize'] = 8 - rcParams['axes.titlesize'] = 8 - rcParams['axes.labelsize'] = 8 - rcParams['ytick.labelsize'] = 8 - rcParams['xtick.labelsize'] = 8 - rcParams['text.usetex'] = True + + rcParams["figure.figsize"] = (3, 4) + rcParams["figure.dpi"] = 300 + rcParams["font.family"] = "sans-serif" + rcParams["font.size"] = 8 + rcParams["legend.fontsize"] = 8 + rcParams["axes.titlesize"] = 8 + rcParams["axes.labelsize"] = 8 + rcParams["ytick.labelsize"] = 8 + rcParams["xtick.labelsize"] = 8 + rcParams["text.usetex"] = True fig = plt.figure(num=3) plt.clf() @@ -269,18 +285,26 @@ def load_data(neuron_model, N, data_path_root): ax1.set_xlim(Ns[0], Ns[-1]) ax1.set_xticklabels([]) ax1.set_ylabel(r"simulation time (s)") - #ax1.set_title(r"fixed in-degree $K=1250$") + # ax1.set_title(r"fixed in-degree $K=1250$") ax1.legend(loc=2) - + ## firing rate ax2.plot( - Ns, rate[cm, :], "-o", mfc=clrs[cm], mec=clrs[cm], ms=ms, lw=lw, color=clrs[cm], label=r"\texttt{%s}" % neuron_model + Ns, + rate[cm, :], + "-o", + mfc=clrs[cm], + mec=clrs[cm], + ms=ms, + lw=lw, + color=clrs[cm], + label=r"\texttt{%s}" % neuron_model, ) ax2.set_xlim(Ns[0], Ns[-1]) ax2.set_xticklabels([]) ax2.set_ylim(0.5, 2) ax2.set_ylabel(r"firing rate (1/s)") - #ax2.legend(loc=1) + # ax2.legend(loc=1) ## weight stat if cm == 0: @@ -309,4 +333,4 @@ def load_data(neuron_model, N, data_path_root): ax3.legend(loc=1) plt.subplots_adjust(left=0.17, bottom=0.1, right=0.95, top=0.95, hspace=0.1) - plt.savefig("../figures/scaling.png") + plt.savefig("figures/scaling.png") From 9488ce89e2bdcf69155ec0eef378f0a8d34cd9a4 Mon Sep 17 00:00:00 2001 From: Jessica Mitchell Date: Mon, 5 Aug 2024 13:40:24 +0200 Subject: [PATCH 29/38] modify Snakefile, rename, use python3; update README, model desc and workflow for snakemake --- pynest/examples/ignore_and_fire/README.rst | 104 ++- pynest/examples/ignore_and_fire/Snakefile | 43 +- .../examples/ignore_and_fire/code/README.rst | 5 - .../twopop_stdp_network_model.rst | 608 ++++++++++++++++++ pynest/examples/ignore_and_fire/workflow.rst | 30 + 5 files changed, 733 insertions(+), 57 deletions(-) delete mode 100644 pynest/examples/ignore_and_fire/code/README.rst create mode 100644 pynest/examples/ignore_and_fire/twopop_stdp_network_model.rst create mode 100644 pynest/examples/ignore_and_fire/workflow.rst diff --git a/pynest/examples/ignore_and_fire/README.rst b/pynest/examples/ignore_and_fire/README.rst index 06a89d82dd..2e23e4581c 100644 --- a/pynest/examples/ignore_and_fire/README.rst +++ b/pynest/examples/ignore_and_fire/README.rst @@ -1,29 +1,55 @@ +.. _experiment-ignore: + Exact scaling experiments using the ``ignore_and_fire`` neuron =============================================================== Background: (Non-) Scalability of recurrent neuronal networks --------------------------------------------------------------- -The verification and validation of neuronal simulation architectures (soft- and hardware) is typically based on models describing networks of neurons. Ideally, such test-case models are scalable with respect to the network size +The verification and validation of neuronal simulation architectures (soft- and hardware) is typically based on +models describing networks of neurons. Ideally, such test-case models are scalable with respect to the network size - to foster a comparison between different computing architectures with different computational resources, -- to be able to extrapolate (up-scale) to networks at brain scale, even if data constrained and well tested network models at this scale are not yet published or existing, and +- to be able to extrapolate (up-scale) to networks at brain scale, even if data constrained and well tested + network models at this scale are not yet published or existing, and - to be able to study and compare different plasticity mechanisms with slow dynamics (down-scaling). -Biological neuronal networks are characterized by a high degree of recurrency. As shown in [1]_, scaling the number of nodes or edges in a recurrent neuronal networks generally alters the network dynamics, such as the average activity level or the structure of correlations. Preserving certain dynamical features by adjusting other parameters can only be achieved in limited ranges or exceptional cases. Recurrent neuronal networks are hence not truly scalable. In this example, we demonstrate how the :doc:`ignore_and_fire ` neuron can help to perform exact scaling experiments with arbitrary types of networks. +Biological neuronal networks are characterized by a high degree of recurrency. As shown in [1]_, scaling the number of nodes or edges in a recurrent neuronal +networks generally alters the network dynamics, such as the average activity level or the structure of correlations. +Preserving certain dynamical features by adjusting other parameters can only be achieved in limited ranges or exceptional cases. Recurrent neuronal net +works are hence not truly scalable. In this example, we demonstrate how the :doc:`ignore_and_fire ` neuron can help to perform +exact scaling experiments with arbitrary types of networks. + Network model ------------- -In this example, we employ a simple network model describing the dynamics of a local cortical circuit at the spatial scale of ~1mm within a single cortical layer. It is derived from the model proposed in [2]_, but accounts for the synaptic weight dynamics for connections between excitatory neurons. The weight dynamics are described by the spike-timing-dependent plasticity (STDP) model derived in [8]_. The model provides a mechanism underlying the formation of broad distributions of synaptic weights in combination with asynchronous irregular spiking activity (see figure below). +In this :doc:`example `, we employ a simple network model describing the dynamics +of a local cortical circuit at the spatial scale of ~1mm within a single cortical layer. It is derived from the model +proposed in [2]_, but accounts for the synaptic weight dynamics for connections between excitatory neurons. The weight +dynamics are described by the spike-timing-dependent plasticity (STDP) model derived in [8]_. The model provides a +mechanism underlying the formation of broad distributions of synaptic weights in combination with asynchronous +irregular spiking activity (see figure below). -A variant of this model, the :doc:`hpc_benchmark `, has been used in a number of benchmarking studies, in particular for weak-scaling experiments ([3]_, [4]_, [5]_, [6]_, [7]_). Due to its random homogeneous connectivity, the model represents a hard benchmarking scenario: each neuron projects with equal probability to any other neuron in the network. Implementations of this model can therefore not exploit any spatial connectivity patterns. In contrast to the model used here, the plasticity dynamics in the ``hpc_benchmark`` is parameterized such that it has only a weak effect on the synaptic weights and, hence, the network dynamics. Here, the effect of the synaptic plasticity is substantial and leads to a significant broadening of the weight distribution (see figure below). Synaptic weights thereby become a sensitive target metric for verification and validation studies. +A variant of this model, the :doc:`hpc_benchmark `, has been used in a number of +benchmarking studies, in particular for weak-scaling experiments ([3]_, [4]_, [5]_, [6]_, [7]_). Due to its random +homogeneous connectivity, the model represents a hard benchmarking scenario: each neuron projects with equal probability +to any other neuron in the network. Implementations of this model can therefore not exploit any spatial connectivity +patterns. In contrast to the model used here, the plasticity dynamics in the ``hpc_benchmark`` is parameterized such +that it has only a weak effect on the synaptic weights and, hence, the network dynamics. Here, the effect of the +synaptic plasticity is substantial and leads to a significant broadening of the weight distribution (see figure below). +Synaptic weights thereby become a sensitive target metric for verification and validation studies. Comparison between the networks with ``integrate-and-fire`` and ``ignore-and-fire`` dynamics -------------------------------------------------------------------------------------------- -The model employed here can be configured into a truly scalable mode by replacing the integrate-and-fire neurons by an :doc:`ignore_and_fire ` dynamics. By doing so, the spike generation dynamics is decoupled from the input integration and the plasticity dynamics; the overall network activity, and, hence, the communication load, is fully controlled by the user. The firing rates and phases of the :doc:`ignore_and_fire ` model are randomly drawn from uniform distributions to guarantee asynchronous spiking activity. The plasticity dynamics remains intact (see figure below). +The model employed here can be configured into a truly scalable mode by replacing the integrate-and-fire neurons by an +:doc:`ignore_and_fire ` dynamics. By doing so, the spike generation dynamics is decoupled +from the input integration and the plasticity dynamics; the overall network activity, and, hence, the communication +load, is fully controlled by the user. The firing rates and phases of the :doc:`ignore_and_fire ` +model are randomly drawn from uniform distributions to guarantee asynchronous spiking activity. The plasticity dynamics +remains intact (see figure below). ================== ===================== ``iaf_psc_alpha`` ``ignore_and_fire`` @@ -33,37 +59,63 @@ The model employed here can be configured into a truly scalable mode by replacin ================== ===================== -.. |iaf_spikes| image:: figures/TwoPopulationNetworkPlastic_iaf_psc_alpha_spikes.png -.. |iaf_weight| image:: figures/TwoPopulationNetworkPlastic_iaf_psc_alpha_weight_distributions.png -.. |ign_spikes| image:: figures/TwoPopulationNetworkPlastic_ignore_and_fire_spikes.png -.. |ign_weight| image:: figures/TwoPopulationNetworkPlastic_ignore_and_fire_weight_distributions.png +.. |iaf_spikes| image:: /static/img/TwoPopulationNetworkPlastic_iaf_psc_alpha_spikes.png +.. |iaf_weight| image:: /static/img/TwoPopulationNetworkPlastic_iaf_psc_alpha_weight_distributions.png +.. |ign_spikes| image:: /static/img/TwoPopulationNetworkPlastic_ignore_and_fire_spikes.png +.. |ign_weight| image:: /static/img/TwoPopulationNetworkPlastic_ignore_and_fire_weight_distributions.png -Spiking activity (top) and distributions of excitatory synaptic weights (bottom) for the network with integrate-and-fire (``iaf_psc_alpha_nest``) and :doc:`ignore_and_fire ` dynamics (``ignore_and_fire``). Figures generated using :doc:`code/generate_reference_data.py ` and :doc:`code generate_reference_figures.py `. +Spiking activity (top) and distributions of excitatory synaptic weights (bottom) for the network with integrate-and-fire +(``iaf_psc_alpha_nest``) and :doc:`ignore_and_fire ` dynamics (``ignore_and_fire``). Figures +generated using :doc:`code/generate_reference_data-ignore_and_fire.py ` +and :doc:`code generate_reference_figuresi-ignore_and_fire.py `. Scaling experiments ------------------- -The ``ignore_and_fire`` variant of the network model permits exact scaling experiments, without the need for any parameter tuning when changing the network size (see figure below). We demonstrate this here by simulating the network for various network sizes between :math:`N=1250` and :math:`N=13750`. +The ``ignore_and_fire`` variant of the network model permits exact scaling experiments, without the need for any +parameter tuning when changing the network size (see figure below). We demonstrate this here by simulating the network +for various network sizes between :math:`N=1250` and :math:`N=13750`. The number of incoming connections per neuron, the in-degree, is kept constant at :math:`K=1250`. -The total number of connections hence scales linearly with :math:`N`. For each simulation, we monitor the simulation (wall clock) time, the time and population averaged firing rate, and the mean standard deviation of the excitatory synaptic weights at the end of the simulation. +The total number of connections hence scales linearly with :math:`N`. For each simulation, we monitor the simulation +(wall clock) time, the time and population averaged firing rate, and the mean standard deviation of the excitatory +synaptic weights at the end of the simulation. -For the integrate-and-fire variant of the network model, the firing rate and the synaptic-weight distribution depend on the network size :math:`N`. In particular for small :math:`N`, the firing rates and the synaptic weights increase due to the denser connectivity. For the ignore-and-fire version, in contrast, the dynamics is independent of the network size. The average firing rate is --by definition-- constant. As the firing phases of the ignore-and-fire neurons are chosen randomly, the spiking activity is asynchronous, irrespective of the connection density. As a consequence, the distribution of synaptic weights (which is shaped by cross-correlations in the spiking activity) remains constant, too. +For the integrate-and-fire variant of the network model, the firing rate and the synaptic-weight distribution depend +on the network size :math:`N`. In particular for small :math:`N`, the firing rates and the synaptic weights increase +due to the denser connectivity. For the ignore-and-fire version, in contrast, the dynamics is independent of the +network size. The average firing rate is --by definition-- constant. As the firing phases of the ignore-and-fire +neurons are chosen randomly, the spiking activity is asynchronous, irrespective of the connection density. As a +consequence, the distribution of synaptic weights (which is shaped by cross-correlations in the spiking activity) remains constant, too. -With the ignore-and-fire version of the model, performance benchmarking studies can thus be performed under better defined conditions. For example, the overall communication load, i.e., the total number of spikes that need to be sent across the network within each time interval, is fully controlled by the user. +With the ignore-and-fire version of the model, performance benchmarking studies can thus be performed under better +defined conditions. For example, the overall communication load, i.e., the total number of spikes that need to be sent +across the network within each time interval, is fully controlled by the user. -.. figure:: figures/scaling.png +.. figure:: /static/img/scaling_ignore_and_fire.png :scale: 50% -Dependence of the simulation time (top), the time and population averaged firing rate (middle) and the excitatory synaptic weights (bottom) on the network size :math:`N` for the\ ``integrate-and-fire`` (black) and the ``ignore-and-fire`` variant of the network model (gray). The in-degree :math:`K=1250` is fixed. Figure generated using :doc:`code/scaling.py `. +Dependence of the simulation time (top), the time and population averaged firing rate (middle) and the excitatory +synaptic weights (bottom) on the network size :math:`N` for the\ ``integrate-and-fire`` (black) and the +``ignore-and-fire`` variant of the network model (gray). The in-degree :math:`K=1250` is fixed. Figure generated using +:doc:`scaling.py `. + +Run the simulations +------------------- + +You can run the simulations by using the provided Snakefile: + * :doc:`Snakefile `: Run simulation workflow -Running the simulations ------------------------- +.. seealso:: -The workflows underlying the numerical experiments shown above are documented in the `Snakefile `. + * :doc:`Detailed description of the network model and parameters ` + * :doc:`NEST simulation details ` + * :doc:`model.py `: NEST implementation of the network model + * :doc:`parameter_dicts.py `: parameter setting + * :doc:`ignore_and_fire model documentation ` References @@ -99,24 +151,14 @@ References .. [7] Kunkel S, Schenck W (2017). The NEST dry-run mode: Efficient dynamic analysis of neuronal network simulation code. Frontiers in Neuroinformatics 11:40. `: simulation workflow - * :doc:`Detailed description of the network model and parameters ` - * :doc:`NEST implementation details ` - * :doc:`code/model.py `: NEST implementation of the network model - * :doc:`code/parameter_dicts.py `: parameter setting - * :doc:`ignore_and_fire model documentation ` - .. toctree:: :hidden: :glob: - doc/* + * diff --git a/pynest/examples/ignore_and_fire/Snakefile b/pynest/examples/ignore_and_fire/Snakefile index fd23827c6e..6dfcca8b03 100644 --- a/pynest/examples/ignore_and_fire/Snakefile +++ b/pynest/examples/ignore_and_fire/Snakefile @@ -1,3 +1,4 @@ + rule all: """ Runs simulations and creates all figures for the ignore-and-fire usecase documentation. @@ -18,26 +19,26 @@ rule generate_reference_data_LIF: Simulates network with LIF neurons, and stores spikes and synaptic weights. """ input: - 'code/model.py', - 'code/parameter_dicts.py', - 'code/generate_reference_data.py' + 'model-ignore_and_fire.py', + 'parameter_dicts-ignore_and_fire.py', + 'generate_reference_data-ignore_and_fire.py' output: temp('.LIF_simulation_done') run: - shell('cd code; python generate_reference_data.py iaf_psc_alpha; cd ..; touch {output}') + shell('python3 generate_reference_data-ignore_and_fire.py iaf_psc_alpha; touch {output}') rule generate_reference_data_IgAF: """ Simulates network with ignore-and-fire neurons, and stores spikes and synaptic weights. """ input: - 'code/model.py', - 'code/parameter_dicts.py', - 'code/generate_reference_data.py' + 'model-ignore_and_fire.py', + 'parameter_dicts-ignore_and_fire.py', + 'generate_reference_data-ignore_and_fire.py' output: temp('.IgAF_simulation_done') run: - shell('cd code; python generate_reference_data.py ignore_and_fire; cd .. ; touch {output}') + shell('python3 generate_reference_data-ignore_and_fire.py ignore_and_fire; touch {output}') rule generate_reference_figures_LIF: """ @@ -46,9 +47,9 @@ rule generate_reference_figures_LIF: with LIF neurons. """ input: - 'code/model.py', - 'code/parameter_dicts.py', - 'code/generate_reference_figures.py', + 'model-ignore_and_fire.py', + 'parameter_dicts-ignore_and_fire.py', + 'generate_reference_figures-ignore_and_fire.py', '.LIF_simulation_done' output: 'figures/TwoPopulationNetworkPlastic_iaf_psc_alpha_spikes.png', @@ -56,7 +57,7 @@ rule generate_reference_figures_LIF: 'figures/TwoPopulationNetworkPlastic_iaf_psc_alpha_connectivity_presim.png', 'figures/TwoPopulationNetworkPlastic_iaf_psc_alpha_connectivity_postsim.png', run: - shell('cd code; python generate_reference_figures.py iaf_psc_alpha') + shell('python3 generate_reference_figures-ignore_and_fire.py iaf_psc_alpha') rule generate_reference_figures_IgAF: """ @@ -65,9 +66,9 @@ rule generate_reference_figures_IgAF: with ignore_and_fire neurons. """ input: - 'code/model.py', - 'code/parameter_dicts.py', - 'code/generate_reference_figures.py', + 'model-ignore_and_fire.py', + 'parameter_dicts-ignore_and_fire.py', + 'generate_reference_figures-ignore_and_fire.py', '.IgAF_simulation_done' output: 'figures/TwoPopulationNetworkPlastic_ignore_and_fire_spikes.png', @@ -75,7 +76,7 @@ rule generate_reference_figures_IgAF: 'figures/TwoPopulationNetworkPlastic_ignore_and_fire_connectivity_presim.png', 'figures/TwoPopulationNetworkPlastic_ignore_and_fire_connectivity_postsim.png', run: - shell('cd code; python generate_reference_figures.py ignore_and_fire') + shell('python3 generate_reference_figures-ignore_and_fire.py ignore_and_fire') rule scaling_experiment: """ @@ -84,14 +85,14 @@ rule scaling_experiment: and synaptic weights for a range of network sizes. """ input: - 'code/model.py', - 'code/parameter_dicts.py', - 'code/scaling.py' + 'model-ignore_and_fire.py', + 'parameter_dicts-ignore_and_fire.py', + 'scaling-ignore_and_fire.py' output: 'figures/scaling.png' run: - shell('cd code; python scaling.py') + shell('python3 scaling-ignore_and_fire.py') rule clean: run: shell("rm figures/*") - shell("rm -rf code/data/*") + shell("rm -rf data/*") diff --git a/pynest/examples/ignore_and_fire/code/README.rst b/pynest/examples/ignore_and_fire/code/README.rst deleted file mode 100644 index a81c545511..0000000000 --- a/pynest/examples/ignore_and_fire/code/README.rst +++ /dev/null @@ -1,5 +0,0 @@ -README -====== - - -.. Do not delete required for Sphinx generation of example diff --git a/pynest/examples/ignore_and_fire/twopop_stdp_network_model.rst b/pynest/examples/ignore_and_fire/twopop_stdp_network_model.rst new file mode 100644 index 0000000000..ad961e3794 --- /dev/null +++ b/pynest/examples/ignore_and_fire/twopop_stdp_network_model.rst @@ -0,0 +1,608 @@ +.. _sec_model_description: + +Two population STDP network model +================================= + +Network model +------------- + +In this example, we employ a simple network model describing the dynamics of a local cortical circuit at the spatial scale of +~1mm within a single cortical layer. It is derived from the model proposed in [1]_, +but accounts for the synaptic weight dynamics for connections between excitatory neurons. The weight dynamics are described +by the spike-timing-dependent plasticity (STDP) model derived in [2]_. +This network model is used as the basis for scaling experiments comparing the model ``ignore-and-fire`` with the ``iaf_psc_alpha``. +You can find + +Summary of network model +------------------------ + +.. list-table:: + :stub-columns: 1 + + * - **Populations** + - excitatory population :math:`\mathcal{E}`, inhibitory population :math:`\mathcal{I}`, external Poissonian spike sources :math:`\mathcal{X}` + * - **Connectivity** + - sparse random connectivity respecting Dale’s principle + * - **Neurons** + - leaky integrate-and-fire (LIF) + * - **Synapses** + - linear input integration with alpha-function-shaped postsynaptic currents (PSCs), spike-timing dependent plasticity (STDP) for connections between excitatory neurons + * - **Input** + - stationary, uncorrelated Poissonian spike trains + + * - + - .. figure:: /static/img/NetworkSketch_TwoPopulationNetworkPlastic.svg + + Network sketch (see `Fig. 8 `_ + in Senk et al. [3]_). + +Detailed desciption of network model +------------------------------------ + +Populations +~~~~~~~~~~~ + +.. table:: + + +---------------------+----------------------+----------------------------------+ + | **Name** | **Elements** | **Size** | + +=====================+======================+==================================+ + | :math:`\mathcal{E}` | LIF neurons | :math:`N_\text{E}=\beta{}N` | + | | | | + +---------------------+----------------------+----------------------------------+ + | :math:`\mathcal{I}` | LIF neurons | :math:`N_\text{I}=N-N_\text{E}` | + | | | | + +---------------------+----------------------+----------------------------------+ + | :math:`\mathcal{X}` | realizations of a | :math:`N` | + | | Poisson point | | + | | process | | + +---------------------+----------------------+----------------------------------+ + +Connectivity +~~~~~~~~~~~~ + +.. table:: + + +---------------------+----------------------+---------------------------------------------------------+ + | **Source** | **Target** | **Pattern** | + +=====================+======================+=========================================================+ + | :math:`\mathcal{E}` | :math:`\mathcal{E}` | - random, | + | | | independent; | + | | | homogeneous | + | | | in-degree | + | | | :math:`K_{\text{E},i}=K_\text{E}` | + | | | | + | | | (:math:`\forall{}i\in\mathcal{E}`) | + | | | | + | | | | + | | | - plastic synaptic weights | + | | | :math:`J_{ij}(t)` | + | | | | + | | | (:math:`\forall{}i\in\mathcal{E},j\in\mathcal{E}`) | + | | | | + | | | - homogeneous | + | | | | + | | | spike-transmission | + | | | delays | + | | | :math:`d_{ij}=d` | + | | | | + | | | (:math:`\forall{}i\in\mathcal{E},j\in\mathcal{E}`) | + | | | | + +---------------------+----------------------+---------------------------------------------------------+ + | :math:`\mathcal{E}` | :math:`\mathcal{I}` | - random, | + | | | independent; | + | | | homogeneous | + | | | in-degree | + | | | :math:`K_{\text{E},i}=K_\text{E}` | + | | | | + | | | (:math:`\forall{}i\in\mathcal{I}`) | + | | | | + | | | | + | | | - fixed synaptic | + | | | weights | + | | | :math:`J_{ij}\in\{0,J\}` | + | | | | + | | | (:math:`\forall{}i\in\mathcal{I},j\in\mathcal{E}`) | + | | | | + | | | | + | | | | + | | | - homogeneous | + | | | | + | | | spike-transmission | + | | | delays | + | | | :math:`d_{ij}=d` | + | | | | + | | | (:math:`\forall{}i\in\mathcal{I},j\in\mathcal{E}`) | + | | | | + +---------------------+----------------------+---------------------------------------------------------+ + | :math:`\mathcal{I}` | :math:`\mathcal \ | - random, | + | | {E}\cup\mathcal{I}` | independent; | + | | | homogeneous | + | | | in-degree | + | | | :math:`K_{\text{I},i}=K_\text{I}` | + | | | | + | | | (:math:`forall{}i\in\mathcal{E}\cup\mathcal{I}`)\ | + | | | j\in\mathcal{I}`) | + | | | | + | | | - fixed synaptic | + | | | weights | + | | | :math:`J_{ij}\in\{-gJ,0\}` | + | | | | + | | | (:math:`\forall{}i\in\mathcal{E}\cup\mathcal{I}, \ | + | | | j\in\mathcal{I}`) | + | | | | + | | | | + | | | - homogeneous | + | | | | + | | | spike-transmission | + | | | delays | + | | | :math:`d_{ij}=d` | + | | | | + | | | (:math:`\forall{}i\in\mathcal{E}\cup\mathcal{I}, \ | + | | | j\in\mathcal{I}`) | + | | | | + | | | | + +---------------------+----------------------+---------------------------------------------------------+ + | :math:`\mathcal{X}` | :math:`\mathcal \ | - one-to-one | + | | {E}\cup\mathcal{I}` | | + | | | - fixed synaptic | + | | | weights | + | | | :math:`J_{ij}=J` | + | | | | + | | | (:math:`\forall{}i\in\mathcal{E}\cup\mathcal{I}, \ | + | | | j\in\mathcal{X}`) | + | | | | + | | | - homogeneous | + | | | | + | | | spike-transmission | + | | | delays | + | | | :math:`d_{ij}=d` | + | | | | + | | | (:math:`\forall{}i\in\mathcal{E}\cup\mathcal{I}, \ | + | | | j\in\mathcal{X}`) | + | | | | + +---------------------+----------------------+---------------------------------------------------------+ + + + +Neuron +~~~~~~ + +.. list-table:: + + * - **Leaky integrate-and-fire (iaf) dynamics** + - Dynamics of membrane potential :math:`V_{i}(t)` and + spiking activity :math:`s_i(t)` of neuro :math:`i\in\left\{1,\ldots,N\right\}`: + + * emission of :math:`k`\ th (:math:`k=1,2,\ldots`) spike of neuron + :math:`i` at time :math:`t_{i}^{k}` if + + .. math:: + V_{i}\left(t_{i}^{k}\right)\geq\theta + + with spike threshold :math:`\theta` + + * reset and refractoriness: + + .. math:: \forall{}k,\ \forall t \in \left(t_{k}^{i},\,t_{k}^{i}+\tau_\text{ref}\right]:\quad V_{i}(t)=V_\text{reset} + + with refractory period :math:`\tau_\text{ref}` and reset potential + :math:`V_\text{reset}` + + * spike train :math:`\displaystyle s_i(t)=\sum_k \delta(t-t_i^k)` + + * subthreshold dynamics of membrane potential :math:`V_{i}(t)`: + + .. math:: + + \begin{aligned} + &\forall{}k,\ \forall t \notin \left[t_{i}^{k},\,t_{i}^{k}+\tau_\text{ref}\right):\\ + &\qquad\tau_\text{m}\frac{\text{d}{}V_i(t)}{\text{d}{}t} = + \Bigl[E_\text{L}-V_i(t)\Bigr]+R_\text{m}I_i(t) + \end{aligned} + + with membrane time constant :math:`\tau_\text{m}`, membrane + resistance :math:`R_\text{m}`, resting potential :math:`E_\text{L}`, + and total synaptic input current :math:`I_i(t)` + + +Synapse: transmission +~~~~~~~~~~~~~~~~~~~~~ + +.. list-table:: + + * - **Current-based synapses with alpha-function shaped postsynaptic currents (PSCs)** + + + - Total synaptic input current of neuron :math:`i` + + .. math:: I_i(t)=I_{\text{E},i}(t)+I_{\text{I},i}(t)+I_{\text{X},i}(t) + + * excitatory, inhibitory and external synaptic input currents + + .. math:: + + %I_{P,i}(t)=\sum_{j\in\mathcal{P}}(\text{PSC}_{ij}*s_j)(t) + %\quad\text{for}\quad + %(P,\mathcal{P})\in\{(\exc,\Epop),(\inh,\Ipop),(\ext,\Xpop)\} + %, + \begin{aligned} + I_{\text{E},i}(t)&=\sum_{j\in\mathcal{E}}\bigl(\text{PSC}_{ij}*s_j\bigr)(t-d_{ij})\\ + I_{\text{I},i}(t)&=\sum_{j\in\mathcal{I}}\bigl(\text{PSC}_{ij}*s_j\bigr)(t-d_{ij})\\ + I_{\text{X},i}(t)&=\sum_{j\in\mathcal{X}}\bigl(\text{PSC}_{ij}*s_j\bigr)(t-d_{ij}) + \end{aligned} + + with spike trains :math:`s_j(t)` of local + (:math:`j\in\mathcal{E}\cup\mathcal{I}`) and external sources + (:math:`j\in\mathcal{X}`), spike transmission delays :math:`d_{ij}`, + and convolution operator “:math:`*`”: + :math:`\displaystyle\bigl(f*g\bigr)(t)=\int_{-\infty}^\infty\text{d}s\,f(s)g(t-s)`) + + * alpha-function shaped postsynaptic currents + + .. math:: \text{PSC}_{ij}(t)=\hat{I}_{ij}e\tau_\text{s}^{-1}te^{-t/\tau_\text{s}}\Theta(t) + + with synaptic time constant :math:`\tau_\text{s}` and Heaviside + function :math:`\Theta(\cdot)` + + * postsynaptic potential triggered by a single presynaptic spike + + .. math:: + + \text{PSP}_{ij}(t)= + \hat{I}_{ij}\frac{e}{\tau_\text{s}C_\text{m}} + \left(\frac{1}{\tau_\text{m}}-\frac{1}{\tau_\text{s}}\right)^{-2} + \left(\left(\frac{1}{\tau_\text{m}}-\frac{1}{\tau_\text{s}}\right) t e^{-t/\tau_\text{s}} - e^{-t/\tau_\text{s}} + e^{-t/\tau_\text{m}} \right) \Theta(t) + + * PSC amplitude (synaptic weight) + + .. math:: + + \hat{I}_{ij}=\text{max}_t\bigl(\text{PSC}_{ij}(t)\bigr) + =\frac{J_{ij}}{J_\text{unit}(\tau_\text{m},\tau_\text{s},C_\text{m})} + + parameterized by PSP amplitude + :math:`J_{ij}=\text{max}_t\bigl(\text{PSP}_{ij}(t)\bigr)` + + with unit PSP amplitude (PSP amplitude for :math:`\hat{I}_{ij}=1`): + + .. math:: + + J_\text{unit}(\tau_\text{m},\tau_\text{s},C_\text{m}) + = \frac{e}{C_\text{m}\left(1-\frac{\tau_\text{s}}{\tau_\text{m}}\right)}\left( \frac{e^{-t_\text{max}/\tau_\text{m}} - e^{-t_\text{max}/\tau_\text{s}}}{\frac{1}{\tau_\text{s}} - \frac{1}{\tau_\text{m}}} - t_\text{max}e^{-t_\text{max}/\tau_\text{s}} \right), + + time to PSP maximum + + .. math:: + + t_\text{max} = + \frac{1}{\frac{1}{\tau_\text{s}} - \frac{1}{\tau_\text{m}}}\left(-W_{-1}\left(\frac{-\tau_\text{s}e^{-\frac{\tau_\text{s}}{\tau_\text{m}}}}{\tau_\text{m}}\right) - \frac{\tau_\text{s}}{\tau_\text{m}}\right), + + and Lambert-W function :math:`\displaystyle W_{-1}(x)` for + :math:`\displaystyle x \ge -1/e` + + + +Synapse: plasticity +~~~~~~~~~~~~~~~~~~~ + +.. list-table:: + + * - **Spike-timing dependent plasticity (STDP) with power-law weight dependence and all-to-all spike pairing scheme.** + See Morrison et al. [2]_ for connections between excitatory neurons. + + + - Dynamics of synaptic weights :math:`J_{ij}(t)` :math:`\forall{}i\in\mathcal{E}, j\in\mathcal{E}`: + + .. math:: + + \begin{aligned} + &\forall J_{ij}\ge{}0: \\[1ex] + &\quad + \frac{\text{d}}{}J_{ij}{\text{d}{}t}= + \lambda^+f^+(J_{ij})\sum_k x^+_j(t)\delta\Bigl(t-[t_i^k+d_{ij}]\Bigr) + + \lambda^-f^-(J_{ij})\sum_l x^-_i(t)\delta\Big(t-[t_j^l-d_{ij}]\Bigr)\\[1ex] + &\forall{}\{t|J_{ij}(t)<0\}: \quad J_{ij}(t)=0 \quad \text{(clipping)} + \end{aligned} + + with + + - pre- and postsynaptic spike times :math:`\{t_j^l|l=1,2,\ldots\}` and + :math:`\{t_i^k|k=1,2,\ldots\}`, + + - magnitude :math:`\lambda^+=\lambda` of weight update for causal + firing (postsynaptic spike following presynaptic spikes: + :math:`t_i^k>t_j^l`), + + - magnitude :math:`\lambda^-=-\alpha\lambda` of weight update for + acausal firing (presynaptic spike following postsynaptic spikes: + :math:`t_i^k + +.. [2] Morrison A. Aertsen, A. and Diesmann M. 2007. + Spike-timing-dependent plasticity in balanced random networks. + Neural Computation. 19(6):1437–1467. + +.. [3] Senk J, Kriener B, Djurfeldt M, Voges N, Jiang H-J, Schüttler L, et al. 2022. + Connectivity concepts in neuronal network modeling. PLoS Comput Biol 18(9): e1010086. + https://doi.org/10.1371/journal.pcbi.1010086 diff --git a/pynest/examples/ignore_and_fire/workflow.rst b/pynest/examples/ignore_and_fire/workflow.rst new file mode 100644 index 0000000000..7b478b34ab --- /dev/null +++ b/pynest/examples/ignore_and_fire/workflow.rst @@ -0,0 +1,30 @@ +Workflow using Snakemake +======================== + +A workflow that will run the scaling experiments comparing ``ignore_and_fire`` with the ``iaf_psc_alpha`` is available as a Snakefile. +If you want to reproduce the results, you can use this workflow. + +1. Ensure :ref:`NEST ` and `snakemake `_ are installed in your active environment + +2. Clone the NEST repository if you have not already done so. + + .. code-block:: sh + + git clone git@github.com:/nest-simulator.git + +3. In the nest-simulator repository, go to ``pynest/examples/ignore_and_fire/`` directory. + + .. code-block:: sh + + cd nest-simulator/pynest/examples/ignore_and_fire + +4. Run ``snakemake -cN`` where N is the number of cores you want to use. + + +Get the :download:`Snakefile` + + +.. seealso:: + + * :ref:`sec_model_description` + * :doc:`/auto_examples/ignore_and_fire/index` From 71efc97a7e3d6e29bf7a62e573815225376da05c Mon Sep 17 00:00:00 2001 From: Jessica Mitchell Date: Mon, 5 Aug 2024 13:41:31 +0200 Subject: [PATCH 30/38] update conf with snakefile, and use sketch for example index --- doc/htmldoc/conf.py | 2 +- doc/htmldoc/examples/index.rst | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/doc/htmldoc/conf.py b/doc/htmldoc/conf.py index 3e0f6f3523..539427a218 100644 --- a/doc/htmldoc/conf.py +++ b/doc/htmldoc/conf.py @@ -81,7 +81,7 @@ "gallery_dirs": "auto_examples", "plot_gallery": "False", "download_all_examples": False, - "copyfile_regex": r".*\.rst|.*\.png|.*\.svg", + "copyfile_regex": r".*\.rst|.*\.png|.*\.svg|Snakefile", } # General information about the project. diff --git a/doc/htmldoc/examples/index.rst b/doc/htmldoc/examples/index.rst index 0bb5f7820d..417dc6bb22 100644 --- a/doc/htmldoc/examples/index.rst +++ b/doc/htmldoc/examples/index.rst @@ -243,7 +243,7 @@ PyNEST examples .. grid:: 1 1 2 3 .. grid-item-card:: Performance testing - :img-top: ../static/img/nest_logo-faded.png + :img-top: ../static/img/NetworkSketch_TwoPopulationNetworkPlastic.svg * :doc:`../auto_examples/hpc_benchmark` * :doc:`../auto_examples/ignore_and_fire/index` From 71a3ccb3fb154accafa18817eaf6a195fd858abb Mon Sep 17 00:00:00 2001 From: Jessica Mitchell Date: Mon, 5 Aug 2024 13:43:53 +0200 Subject: [PATCH 31/38] fix missing line --- .../ignore_and_fire/generate_reference_data-ignore_and_fire.py | 1 + .../generate_reference_figures-ignore_and_fire.py | 1 + pynest/examples/ignore_and_fire/model-ignore_and_fire.py | 1 + .../examples/ignore_and_fire/parameter_dict-ignore_and_fire.py | 1 + pynest/examples/ignore_and_fire/scaling-ignore_and_fire.py | 1 + 5 files changed, 5 insertions(+) diff --git a/pynest/examples/ignore_and_fire/generate_reference_data-ignore_and_fire.py b/pynest/examples/ignore_and_fire/generate_reference_data-ignore_and_fire.py index 31762dd6c6..64c02af8a7 100644 --- a/pynest/examples/ignore_and_fire/generate_reference_data-ignore_and_fire.py +++ b/pynest/examples/ignore_and_fire/generate_reference_data-ignore_and_fire.py @@ -1,3 +1,4 @@ +# -*- coding: utf-8 -*- # # generate_reference_data-ignore_and_fire.py # diff --git a/pynest/examples/ignore_and_fire/generate_reference_figures-ignore_and_fire.py b/pynest/examples/ignore_and_fire/generate_reference_figures-ignore_and_fire.py index 408029c008..89816e1f23 100644 --- a/pynest/examples/ignore_and_fire/generate_reference_figures-ignore_and_fire.py +++ b/pynest/examples/ignore_and_fire/generate_reference_figures-ignore_and_fire.py @@ -1,3 +1,4 @@ +# -*- coding: utf-8 -*- # # generate_references_figures-ignore_and_fire.py # diff --git a/pynest/examples/ignore_and_fire/model-ignore_and_fire.py b/pynest/examples/ignore_and_fire/model-ignore_and_fire.py index 0708fb5954..8250282d6a 100644 --- a/pynest/examples/ignore_and_fire/model-ignore_and_fire.py +++ b/pynest/examples/ignore_and_fire/model-ignore_and_fire.py @@ -1,3 +1,4 @@ +# -*- coding: utf-8 -*- # # model-ignore_and_fire.py # diff --git a/pynest/examples/ignore_and_fire/parameter_dict-ignore_and_fire.py b/pynest/examples/ignore_and_fire/parameter_dict-ignore_and_fire.py index 6650ba5a47..a639ab5d41 100644 --- a/pynest/examples/ignore_and_fire/parameter_dict-ignore_and_fire.py +++ b/pynest/examples/ignore_and_fire/parameter_dict-ignore_and_fire.py @@ -1,3 +1,4 @@ +# -*- coding: utf-8 -*- # # parameter_dict-ignore_and_fire.py # diff --git a/pynest/examples/ignore_and_fire/scaling-ignore_and_fire.py b/pynest/examples/ignore_and_fire/scaling-ignore_and_fire.py index 744ca5044e..5d44b61bfd 100644 --- a/pynest/examples/ignore_and_fire/scaling-ignore_and_fire.py +++ b/pynest/examples/ignore_and_fire/scaling-ignore_and_fire.py @@ -1,3 +1,4 @@ +# -*- coding: utf-8 -*- # # scaling-ignore_and_fire.py # From d95649029235847c82b9319adc4415877cd584ee Mon Sep 17 00:00:00 2001 From: Jessica Mitchell Date: Tue, 6 Aug 2024 10:01:29 +0200 Subject: [PATCH 32/38] add link to ignore and fire --- doc/htmldoc/hpc/benchmarking.rst | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/doc/htmldoc/hpc/benchmarking.rst b/doc/htmldoc/hpc/benchmarking.rst index 870baf86a9..913e0c192b 100644 --- a/doc/htmldoc/hpc/benchmarking.rst +++ b/doc/htmldoc/hpc/benchmarking.rst @@ -43,7 +43,10 @@ For more details on the conceptual ideas behind beNNch, refer to Albers et al. ( For further details, see the accompanying `beNNch GitHub Page `_. And for a detailed step-by-step walk though see `Walk through guide `_. - Example PyNEST script: :doc:`../auto_examples/hpc_benchmark` + Example PyNEST scripts: + + * :doc:`../auto_examples/hpc_benchmark` + * :doc:`../auto_examples/ignore_and_fire/index` References From 690f0f9e3cdbb172c1ecbc6a349554035ec984fe Mon Sep 17 00:00:00 2001 From: Jessica Mitchell Date: Tue, 6 Aug 2024 10:18:38 +0200 Subject: [PATCH 33/38] fix headers --- .../generate_reference_figures-ignore_and_fire.py | 2 +- pynest/examples/ignore_and_fire/model-ignore_and_fire.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/pynest/examples/ignore_and_fire/generate_reference_figures-ignore_and_fire.py b/pynest/examples/ignore_and_fire/generate_reference_figures-ignore_and_fire.py index 89816e1f23..2da39f4d5a 100644 --- a/pynest/examples/ignore_and_fire/generate_reference_figures-ignore_and_fire.py +++ b/pynest/examples/ignore_and_fire/generate_reference_figures-ignore_and_fire.py @@ -1,6 +1,6 @@ # -*- coding: utf-8 -*- # -# generate_references_figures-ignore_and_fire.py +# generate_reference_figures-ignore_and_fire.py # # This file is part of NEST. # diff --git a/pynest/examples/ignore_and_fire/model-ignore_and_fire.py b/pynest/examples/ignore_and_fire/model-ignore_and_fire.py index 8250282d6a..57706d537e 100644 --- a/pynest/examples/ignore_and_fire/model-ignore_and_fire.py +++ b/pynest/examples/ignore_and_fire/model-ignore_and_fire.py @@ -1,6 +1,6 @@ # -*- coding: utf-8 -*- # -# model-ignore_and_fire.py +# model-ignore_and_fire.py # # This file is part of NEST. # From 051aba9330aeacdc9cf980cdcbd90c0c64b5512a Mon Sep 17 00:00:00 2001 From: Jessica Mitchell Date: Tue, 6 Aug 2024 10:32:39 +0200 Subject: [PATCH 34/38] header --- .../examples/ignore_and_fire/parameter_dict-ignore_and_fire.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pynest/examples/ignore_and_fire/parameter_dict-ignore_and_fire.py b/pynest/examples/ignore_and_fire/parameter_dict-ignore_and_fire.py index a639ab5d41..ef5deebc5b 100644 --- a/pynest/examples/ignore_and_fire/parameter_dict-ignore_and_fire.py +++ b/pynest/examples/ignore_and_fire/parameter_dict-ignore_and_fire.py @@ -1,6 +1,6 @@ # -*- coding: utf-8 -*- # -# parameter_dict-ignore_and_fire.py +# parameter_dict-ignore_and_fire.py # # This file is part of NEST. # From 62f81e90aadec8f437f75e6914c10be32705542c Mon Sep 17 00:00:00 2001 From: Jessica Mitchell Date: Thu, 8 Aug 2024 13:09:21 +0200 Subject: [PATCH 35/38] resolve pylinting issues --- ...generate_reference_data-ignore_and_fire.py | 12 ++-- ...erate_reference_figures-ignore_and_fire.py | 18 ++--- .../ignore_and_fire/model-ignore_and_fire.py | 65 +++++++++---------- .../parameter_dict-ignore_and_fire.py | 2 +- .../scaling-ignore_and_fire.py | 48 +++++++------- 5 files changed, 70 insertions(+), 75 deletions(-) diff --git a/pynest/examples/ignore_and_fire/generate_reference_data-ignore_and_fire.py b/pynest/examples/ignore_and_fire/generate_reference_data-ignore_and_fire.py index 64c02af8a7..ce34f29433 100644 --- a/pynest/examples/ignore_and_fire/generate_reference_data-ignore_and_fire.py +++ b/pynest/examples/ignore_and_fire/generate_reference_data-ignore_and_fire.py @@ -58,26 +58,24 @@ def generate_reference_data(neuron_model="ignore_and_fire"): model_instance.create() model_instance.connect() - ## connectivity at start of simulation - subset_size = 2000 ## number of pre- and post-synaptic neurons weights are extracted from + # connectivity at start of simulation + subset_size = 2000 # number of pre- and post-synaptic neurons weights are extracted from pop_pre = model_instance.nodes["pop_E"][:subset_size] pop_post = model_instance.nodes["pop_E"][:subset_size] C = model_instance.get_connectivity( pop_pre, pop_post, model_instance.pars["data_path"] + "/" + "connectivity_presim.dat" ) - ## simulate + # simulate model_instance.simulate(model_instance.pars["T"]) - ## save parameters to file + # save parameters to file model_instance.save_parameters("model_instance_parameters", model_instance.pars["data_path"]) - ## connectivity at end of simulation + # connectivity at end of simulation C = model_instance.get_connectivity( pop_pre, pop_post, model_instance.pars["data_path"] + "/" + "connectivity_postsim.dat" ) - return - generate_reference_data(neuron_model=sys.argv[1]) diff --git a/pynest/examples/ignore_and_fire/generate_reference_figures-ignore_and_fire.py b/pynest/examples/ignore_and_fire/generate_reference_figures-ignore_and_fire.py index 2da39f4d5a..d8aff5c214 100644 --- a/pynest/examples/ignore_and_fire/generate_reference_figures-ignore_and_fire.py +++ b/pynest/examples/ignore_and_fire/generate_reference_figures-ignore_and_fire.py @@ -147,7 +147,7 @@ def plot_weight_distributions(whist_presim, whist_postsim, weights, pars, path=" def generate_reference_figures(neuron_model="ignore_and_fire"): """Generate and store set of reference data""" - ## raster plot + # raster plot parameters = model.get_default_parameters() parameters["neuron_model"] = neuron_model @@ -155,14 +155,14 @@ def generate_reference_figures(neuron_model="ignore_and_fire"): parameters["record_spikes"] = True parameters["record_weights"] = True - ## fetch node ids + # fetch node ids model_instance = model.Model(parameters) model_instance.create() - ## create subfolder for figures (if necessary) + # create subfolder for figures (if necessary) os.system("mkdir -p " + model_instance.pars["data_path"]) - ## load spikes from reference data + # load spikes from reference data spikes = model.load_spike_data( model_instance.pars["data_path"], "spikes-%d" % (np.array(model_instance.nodes["spike_recorder"])[0]), @@ -170,30 +170,30 @@ def generate_reference_figures(neuron_model="ignore_and_fire"): # plot_spikes(spikes, model_instance.nodes, model_instance.pars, model_instance.pars["data_path"]) plot_spikes(spikes, model_instance.nodes, model_instance.pars, "figures") - ## load connectivity from reference data + # load connectivity from reference data connectivity_presim = model.load_connectivity_data(model_instance.pars["data_path"], "connectivity_presim") connectivity_postsim = model.load_connectivity_data(model_instance.pars["data_path"], "connectivity_postsim") - ## create connectivity matrices before and after simulation for a subset of neurons + # create connectivity matrices before and after simulation for a subset of neurons subset_size = 100 pop_pre = np.array(model_instance.nodes["pop_E"])[:subset_size] pop_post = np.array(model_instance.nodes["pop_E"])[:subset_size] W_presim, pop_pre, pop_post = model.get_connectivity_matrix(connectivity_presim, pop_pre, pop_post) W_postsim, pop_pre, pop_post = model.get_connectivity_matrix(connectivity_postsim, pop_pre, pop_post) - ## plot connectivity matrices + # plot connectivity matrices # plot_connectivity_matrix(W_presim, pop_pre, pop_post, "_presim", model_instance.pars["data_path"]) # plot_connectivity_matrix(W_postsim, pop_pre, pop_post, "_postsim", model_instance.pars["data_path"]) plot_connectivity_matrix(W_presim, pop_pre, pop_post, model_instance.pars, "_presim", "figures") plot_connectivity_matrix(W_postsim, pop_pre, pop_post, model_instance.pars, "_postsim", "figures") - ## compute weight distributions + # compute weight distributions # weights = np.arange(29.5,34.1,0.05) weights = np.arange(0.0, 150.1, 0.5) whist_presim = model.get_weight_distribution(connectivity_presim, weights) whist_postsim = model.get_weight_distribution(connectivity_postsim, weights) - ## plot weight distributions + # plot weight distributions # plot_weight_distributions(whist_presim, whist_postsim, weights, model_instance.pars["data_path"]) plot_weight_distributions(whist_presim, whist_postsim, weights, model_instance.pars, "figures") diff --git a/pynest/examples/ignore_and_fire/model-ignore_and_fire.py b/pynest/examples/ignore_and_fire/model-ignore_and_fire.py index 57706d537e..e7685e2455 100644 --- a/pynest/examples/ignore_and_fire/model-ignore_and_fire.py +++ b/pynest/examples/ignore_and_fire/model-ignore_and_fire.py @@ -95,6 +95,8 @@ def __init__(self, parameters): Returns ------- + none + """ print("\nInitialising model and simulation...") @@ -143,6 +145,9 @@ def __init__(self, parameters): elif self.pars["neuron_model"] == "ignore_and_fire": self.__neuron_params = {} + else: + return None + def __derived_parameters(self, parameters): """ Set additional parameters derived from base parameters. @@ -185,8 +190,6 @@ def __derived_parameters(self, parameters): if self.pars["N_rec_spikes"] == "all": self.pars["N_rec_spikes"] = self.pars["N"] - return - def create(self): """ Create and configure all network nodes (neurons + recording and stimulus devices), @@ -207,10 +210,12 @@ def create(self): nest.GetLocalNodeCollection(pop_all).V_m = random_vm elif self.pars["neuron_model"] == "ignore_and_fire": # pop_all = nest.Create("ignore_and_fire", self.pars["N"]) # overall population - # pop_all.rate = np.random.uniform(low=self.pars["ignore_and_fire_pars"]["rate_dist"][0],high=self.pars["ignore_and_fire_pars"]["rate_dist"][1],size=self.pars["N"]) - # pop_all.phase = np.random.uniform(low=self.pars["ignore_and_fire_pars"]["phase_dist"][0],high=self.pars["ignore_and_fire_pars"]["phase_dist"][1],size=self.pars["N"]) + # pop_all.rate = np.random.uniform(low=self.pars["ignore_and_fire_pars"]["rate_dist"][0],high=self.pars + # ["ignore_and_fire_pars"]["rate_dist"][1],size=self.pars["N"]) + # pop_all.phase = np.random.uniform(low=self.pars["ignore_and_fire_pars"]["phase_dist"][0], + # high=self.pars["ignore_and_fire_pars"]["phase_dist"][1],size=self.pars["N"]) - ## better, but not working yet: + # better, but not working yet: pop_all.rate = nest.random.uniform( min=self.pars["ignore_and_fire_pars"]["rate_dist"][0], max=self.pars["ignore_and_fire_pars"]["rate_dist"][1], @@ -265,8 +270,6 @@ def create(self): self.nodes["spike_recorder"] = spike_recorder # self.nodes["weight_recorder"] = weight_recorder - return - def connect(self): """ Connect network and devices. @@ -274,7 +277,7 @@ def connect(self): """ print("\nConnecting network and devices...") - ## fetch neuron populations and device ids + # fetch neuron populations and device ids pop_all = self.nodes["pop_all"] pop_E = self.nodes["pop_E"] pop_I = self.nodes["pop_I"] @@ -282,7 +285,7 @@ def connect(self): spike_recorder = self.nodes["spike_recorder"] # connect network - ## EE connections (plastic) + # EE connections (plastic) nest.Connect( pop_E, pop_E, @@ -295,7 +298,7 @@ def connect(self): syn_spec="excitatory_plastic", ) - ## EI connections (static) + # EI connections (static) nest.Connect( pop_E, pop_I, @@ -308,7 +311,7 @@ def connect(self): syn_spec="excitatory_static", ) - ## IE and II connections (static) + # IE and II connections (static) nest.Connect( pop_I, pop_all, @@ -339,8 +342,6 @@ def connect(self): nest.Prepare() nest.Cleanup() - return - def simulate(self, t_sim): """ Run simulation. @@ -356,8 +357,6 @@ def simulate(self, t_sim): nest.Simulate(t_sim) - return - def save_parameters(self, filename_root, path): """ Save model-instance parameters to file. @@ -376,8 +375,6 @@ def save_parameters(self, filename_root, path): json.dump(self.pars, open("%s/%s.json" % (path, filename_root), "w"), indent=4) - return - def get_connectivity(self, pop_pre, pop_post, filename=None): """ Extract connectivity for subpopulations pop_pre and pop_post @@ -464,7 +461,7 @@ def get_data_file_list(path, label): """ - ## get list of files names + # get list of files names files = [] for file_name in os.listdir(path): if file_name.endswith(".dat") and file_name.startswith(label): @@ -513,14 +510,14 @@ def load_spike_data(path, label, time_interval=None, pop=None, skip_rows=3): files = get_data_file_list(path, label) - ## open spike files and read data + # open spike files and read data spikes = [] for file_name in files: try: spikes += [ np.loadtxt("%s/%s" % (path, file_name), skiprows=skip_rows) - ] ## load spike file while skipping the header - except: + ] # load spike file while skipping the header + except ValueError: print("Error: %s" % sys.exc_info()[1]) print( "Remove non-numeric entries from file %s (e.g. in file header) \ @@ -530,8 +527,8 @@ def load_spike_data(path, label, time_interval=None, pop=None, skip_rows=3): spikes = np.concatenate(spikes) - ## extract spikes in specified time interval - if time_interval != None: + # extract spikes in specified time interval + if time_interval is not None: if type(time_interval) == tuple: ind = (spikes[:, 1] >= time_interval[0]) * (spikes[:, 1] <= time_interval[1]) spikes = spikes[ind, :] @@ -540,7 +537,7 @@ def load_spike_data(path, label, time_interval=None, pop=None, skip_rows=3): if type(pop) == nest.NodeCollection: spikes_subset = [] - for cn, nid in enumerate(pop): ## loop over all neurons + for cn, nid in enumerate(pop): # loop over all neurons print( "Spike extraction from %d/%d (%d%%) neurons completed" % (cn + 1, len(pop), 1.0 * (cn + 1) / len(pop) * 100), @@ -549,7 +546,7 @@ def load_spike_data(path, label, time_interval=None, pop=None, skip_rows=3): ind = np.where(spikes[:, 0] == nid)[0] spikes_subset += list(spikes[ind, :]) spikes = np.array(spikes_subset) - elif pop == None: + elif pop is None: pass else: print("Warning: pop must be a NEST NodeCollection or None. All spikes are loaded.") @@ -571,7 +568,8 @@ def load_connectivity_data(path, label, skip_rows=1): Connectivity file label (file name root). skip_rows: int, optional - Number of rows to be skipped while reading connectivity files (to remove file headers). The default is 1. + Number of rows to be skipped while reading connectivity files (to remove file headers). + The default is 1. Returns ------- @@ -589,15 +587,16 @@ def load_connectivity_data(path, label, skip_rows=1): files = get_data_file_list(path, label) - ## open weight files and read data + # open weight files and read data C = [] for file_name in files: try: - C += [np.loadtxt("%s/%s" % (path, file_name), skiprows=skip_rows)] ## load file while skipping the header - except: + C += [np.loadtxt("%s/%s" % (path, file_name), skiprows=skip_rows)] # load file while skipping the header + except ValueError: print("Error: %s" % sys.exc_info()[1]) print( - "Remove non-numeric entries from file %s (e.g. in file header) by specifying (optional) parameter 'skip_rows'.\n" + "Remove non-numeric entries from file %s (e.g. in file header) by specifying (optional) parameter \ + 'skip_rows'.\n" % (file_name) ) @@ -709,10 +708,10 @@ def get_connectivity_matrix(connectivity, pop_pre=[], pop_post=[]): if len(pop_post) == 0: pop_post = np.unique(connectivity[:, 1]) - ## initialise weight matrix - W = np.zeros([len(pop_post), len(pop_pre)]) ## convention: pre = columns, post = rows + # initialise weight matrix + W = np.zeros([len(pop_post), len(pop_pre)]) # convention: pre = columns, post = rows - ## fill weight matrix + # fill weight matrix for c in range(connectivity.shape[0]): W[get_index(pop_post, connectivity[c, 1]), get_index(pop_pre, connectivity[c, 0])] = connectivity[c, 2] diff --git a/pynest/examples/ignore_and_fire/parameter_dict-ignore_and_fire.py b/pynest/examples/ignore_and_fire/parameter_dict-ignore_and_fire.py index ef5deebc5b..5b8cd3b408 100644 --- a/pynest/examples/ignore_and_fire/parameter_dict-ignore_and_fire.py +++ b/pynest/examples/ignore_and_fire/parameter_dict-ignore_and_fire.py @@ -51,7 +51,7 @@ pars["theta"] = 20.0 # spike threshold(mV) pars["V_reset"] = 0.0 # reset potential(mV) -## needed for ignore_and_fire version of the model +# needed for ignore_and_fire version of the model pars["ignore_and_fire_pars"] = {} pars["ignore_and_fire_pars"]["rate_dist"] = [0.5, 1.5] pars["ignore_and_fire_pars"]["phase_dist"] = [0.01, 1.0] diff --git a/pynest/examples/ignore_and_fire/scaling-ignore_and_fire.py b/pynest/examples/ignore_and_fire/scaling-ignore_and_fire.py index 5d44b61bfd..7d248ec713 100644 --- a/pynest/examples/ignore_and_fire/scaling-ignore_and_fire.py +++ b/pynest/examples/ignore_and_fire/scaling-ignore_and_fire.py @@ -38,11 +38,11 @@ from matplotlib import gridspec ########################################################## -## parameters -neuron_models = ["iaf_psc_alpha", "ignore_and_fire"] ## neuron models -Ns = numpy.arange(1250, 15000, 1250) ## network sizes +# parameters +neuron_models = ["iaf_psc_alpha", "ignore_and_fire"] # neuron models +Ns = numpy.arange(1250, 15000, 1250) # network sizes -data_path_root = "./data/" ## root of path to simulation data +data_path_root = "./data/" # root of path to simulation data simulate = True # simulate = False @@ -51,7 +51,7 @@ ########################################################## -## network construction and simulation +# network construction and simulation def run_model(neuron_model, N, data_path_root): """ Builds an instance of the model for a given network size and neuron model, @@ -61,13 +61,13 @@ def run_model(neuron_model, N, data_path_root): parameters = model.get_default_parameters() - ## overwrite default parameters + # overwrite default parameters parameters["record_spikes"] = True - parameters["neuron_model"] = neuron_model ## choose model flavor - parameters["N"] = N ## specify scale (network sizes) + parameters["neuron_model"] = neuron_model # choose model flavor + parameters["N"] = N # specify scale (network sizes) parameters["data_path"] = data_path_root + "/N" + str(N) - ## create and simulate network + # create and simulate network model_instance = model.Model(parameters) start = time.time() @@ -81,19 +81,19 @@ def run_model(neuron_model, N, data_path_root): stop = time.time() sim_time = stop - start - ## store model instance parameters + # store model instance parameters model_instance.save_parameters("model_instance_parameters", model_instance.pars["data_path"]) - ## record connectivity at end of simulation - subset_size = 200 ## number of pre- and post-synaptic neurons weights are extracted from + # record connectivity at end of simulation + subset_size = 200 # number of pre- and post-synaptic neurons weights are extracted from pop_pre = model_instance.nodes["pop_E"][:subset_size] pop_post = model_instance.nodes["pop_E"][:subset_size] C = model_instance.get_connectivity( pop_pre, pop_post, model_instance.pars["data_path"] + "/" + "connectivity_postsim.dat" ) - ## data analysis - ### time and population averaged firing rate + # data analysis + ## time and population averaged firing rate spikes = model.load_spike_data( model_instance.pars["data_path"], "spikes-%d" % (numpy.array(model_instance.nodes["spike_recorder"])[0]) ) @@ -101,7 +101,7 @@ def run_model(neuron_model, N, data_path_root): spikes, (0.0, model_instance.pars["T"]), model_instance.pars["N_rec_spikes"] ) - ### synaptic weight statistics after simulation + ## synaptic weight statistics after simulation connectivity_postsim = model.load_connectivity_data(model_instance.pars["data_path"], "connectivity_postsim") weight_stats = data_statistics(connectivity_postsim[:, 2]) # weights = numpy.arange(0.,150.1,0.5) @@ -185,8 +185,6 @@ def save_dict_as_json(data_dict, filename_root, path): json.dump(data_dict, open("%s/%s.json" % (path, filename_root), "w")) - return - def load_data(neuron_model, N, data_path_root): """ @@ -194,7 +192,7 @@ def load_data(neuron_model, N, data_path_root): """ data = {} - ## read data from json file + # read data from json file data_path = data_path_root + "/N" + str(N) + "/" + neuron_model with open(data_path + "/data.json") as f: data = json.load(f) @@ -237,7 +235,7 @@ def load_data(neuron_model, N, data_path_root): print() if analyse: - ## plotting + # plotting from matplotlib import rcParams @@ -257,13 +255,13 @@ def load_data(neuron_model, N, data_path_root): gs = gridspec.GridSpec(3, 1) - ## build and sim times + # build and sim times ax1 = fig.add_subplot(gs[0, 0]) - ## firing rate + # firing rate ax2 = fig.add_subplot(gs[1, 0]) - ## weight stat + # weight stat ax3 = fig.add_subplot(gs[2, 0]) ms = 4 @@ -271,7 +269,7 @@ def load_data(neuron_model, N, data_path_root): clrs = ["0", "0.8"] for cm, neuron_model in enumerate(neuron_models): - ## sim time + # sim time ax1.plot( Ns, sim_time[cm, :], @@ -289,7 +287,7 @@ def load_data(neuron_model, N, data_path_root): # ax1.set_title(r"fixed in-degree $K=1250$") ax1.legend(loc=2) - ## firing rate + # firing rate ax2.plot( Ns, rate[cm, :], @@ -307,7 +305,7 @@ def load_data(neuron_model, N, data_path_root): ax2.set_ylabel(r"firing rate (1/s)") # ax2.legend(loc=1) - ## weight stat + # weight stat if cm == 0: lbl1 = "mean" lbl2 = "mean + SD" From 9091b095a5727610499c0f99b9fdd0e26fa933f3 Mon Sep 17 00:00:00 2001 From: Jessica Mitchell Date: Thu, 8 Aug 2024 13:17:56 +0200 Subject: [PATCH 36/38] pylint --- .../generate_reference_data-ignore_and_fire.py | 2 ++ .../generate_reference_figures-ignore_and_fire.py | 4 +--- pynest/examples/ignore_and_fire/model-ignore_and_fire.py | 9 +++------ .../examples/ignore_and_fire/scaling-ignore_and_fire.py | 6 ++---- 4 files changed, 8 insertions(+), 13 deletions(-) diff --git a/pynest/examples/ignore_and_fire/generate_reference_data-ignore_and_fire.py b/pynest/examples/ignore_and_fire/generate_reference_data-ignore_and_fire.py index ce34f29433..856f82bd5a 100644 --- a/pynest/examples/ignore_and_fire/generate_reference_data-ignore_and_fire.py +++ b/pynest/examples/ignore_and_fire/generate_reference_data-ignore_and_fire.py @@ -77,5 +77,7 @@ def generate_reference_data(neuron_model="ignore_and_fire"): pop_pre, pop_post, model_instance.pars["data_path"] + "/" + "connectivity_postsim.dat" ) + return None + generate_reference_data(neuron_model=sys.argv[1]) diff --git a/pynest/examples/ignore_and_fire/generate_reference_figures-ignore_and_fire.py b/pynest/examples/ignore_and_fire/generate_reference_figures-ignore_and_fire.py index d8aff5c214..07d7955f1d 100644 --- a/pynest/examples/ignore_and_fire/generate_reference_figures-ignore_and_fire.py +++ b/pynest/examples/ignore_and_fire/generate_reference_figures-ignore_and_fire.py @@ -30,7 +30,7 @@ import model import nest import numpy as np -from matplotlib import gridspec +from matplotlib import gridspec, rcParams def time_and_population_averaged_spike_rate(spikes, time_interval, pop_size): @@ -198,8 +198,6 @@ def generate_reference_figures(neuron_model="ignore_and_fire"): plot_weight_distributions(whist_presim, whist_postsim, weights, model_instance.pars, "figures") -from matplotlib import rcParams - rcParams["figure.figsize"] = (4, 3) rcParams["figure.dpi"] = 300 rcParams["font.family"] = "sans-serif" diff --git a/pynest/examples/ignore_and_fire/model-ignore_and_fire.py b/pynest/examples/ignore_and_fire/model-ignore_and_fire.py index e7685e2455..812b0c90a6 100644 --- a/pynest/examples/ignore_and_fire/model-ignore_and_fire.py +++ b/pynest/examples/ignore_and_fire/model-ignore_and_fire.py @@ -145,9 +145,6 @@ def __init__(self, parameters): elif self.pars["neuron_model"] == "ignore_and_fire": self.__neuron_params = {} - else: - return None - def __derived_parameters(self, parameters): """ Set additional parameters derived from base parameters. @@ -503,7 +500,7 @@ def load_spike_data(path, label, time_interval=None, pop=None, skip_rows=3): """ - if type(time_interval) == tuple: + if type(time_interval) is tuple: print("Loading spike data in interval (%.1f ms, %.1f ms] ..." % (time_interval[0], time_interval[1])) else: print("Loading spike data...") @@ -529,13 +526,13 @@ def load_spike_data(path, label, time_interval=None, pop=None, skip_rows=3): # extract spikes in specified time interval if time_interval is not None: - if type(time_interval) == tuple: + if type(time_interval) is tuple: ind = (spikes[:, 1] >= time_interval[0]) * (spikes[:, 1] <= time_interval[1]) spikes = spikes[ind, :] else: print("Warning: time_interval must be a tuple or None. All spikes are loaded.") - if type(pop) == nest.NodeCollection: + if type(pop) is nest.NodeCollection: spikes_subset = [] for cn, nid in enumerate(pop): # loop over all neurons print( diff --git a/pynest/examples/ignore_and_fire/scaling-ignore_and_fire.py b/pynest/examples/ignore_and_fire/scaling-ignore_and_fire.py index 7d248ec713..0115a2224a 100644 --- a/pynest/examples/ignore_and_fire/scaling-ignore_and_fire.py +++ b/pynest/examples/ignore_and_fire/scaling-ignore_and_fire.py @@ -93,7 +93,7 @@ def run_model(neuron_model, N, data_path_root): ) # data analysis - ## time and population averaged firing rate + # time and population averaged firing rate spikes = model.load_spike_data( model_instance.pars["data_path"], "spikes-%d" % (numpy.array(model_instance.nodes["spike_recorder"])[0]) ) @@ -101,7 +101,7 @@ def run_model(neuron_model, N, data_path_root): spikes, (0.0, model_instance.pars["T"]), model_instance.pars["N_rec_spikes"] ) - ## synaptic weight statistics after simulation + # synaptic weight statistics after simulation connectivity_postsim = model.load_connectivity_data(model_instance.pars["data_path"], "connectivity_postsim") weight_stats = data_statistics(connectivity_postsim[:, 2]) # weights = numpy.arange(0.,150.1,0.5) @@ -181,8 +181,6 @@ def save_dict_as_json(data_dict, filename_root, path): """ - import json - json.dump(data_dict, open("%s/%s.json" % (path, filename_root), "w")) From 6a469dc8c66ec85934c248548168f6edfd9cc20a Mon Sep 17 00:00:00 2001 From: Jessica Mitchell Date: Thu, 8 Aug 2024 13:22:39 +0200 Subject: [PATCH 37/38] lint --- .../generate_reference_data-ignore_and_fire.py | 8 -------- 1 file changed, 8 deletions(-) diff --git a/pynest/examples/ignore_and_fire/generate_reference_data-ignore_and_fire.py b/pynest/examples/ignore_and_fire/generate_reference_data-ignore_and_fire.py index 856f82bd5a..ee47c21bb0 100644 --- a/pynest/examples/ignore_and_fire/generate_reference_data-ignore_and_fire.py +++ b/pynest/examples/ignore_and_fire/generate_reference_data-ignore_and_fire.py @@ -37,12 +37,6 @@ def generate_reference_data(neuron_model="ignore_and_fire"): parameters = model.get_default_parameters() spikes = model.load_spike_data("./") - Arguments - --------- - - Returns - ------- - """ parameters = model.get_default_parameters() @@ -77,7 +71,5 @@ def generate_reference_data(neuron_model="ignore_and_fire"): pop_pre, pop_post, model_instance.pars["data_path"] + "/" + "connectivity_postsim.dat" ) - return None - generate_reference_data(neuron_model=sys.argv[1]) From 9be496953e700695a0ee9fae237bbdbd5d84e09b Mon Sep 17 00:00:00 2001 From: Jessica Mitchell Date: Thu, 8 Aug 2024 13:31:09 +0200 Subject: [PATCH 38/38] minor text updates --- pynest/examples/ignore_and_fire/README.rst | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/pynest/examples/ignore_and_fire/README.rst b/pynest/examples/ignore_and_fire/README.rst index 2e23e4581c..a3802a73cb 100644 --- a/pynest/examples/ignore_and_fire/README.rst +++ b/pynest/examples/ignore_and_fire/README.rst @@ -24,7 +24,7 @@ exact scaling experiments with arbitrary types of networks. Network model ------------- -In this :doc:`example `, we employ a simple network model describing the dynamics +In this example, we employ a simple network model describing the dynamics of a local cortical circuit at the spatial scale of ~1mm within a single cortical layer. It is derived from the model proposed in [2]_, but accounts for the synaptic weight dynamics for connections between excitatory neurons. The weight dynamics are described by the spike-timing-dependent plasticity (STDP) model derived in [8]_. The model provides a @@ -67,8 +67,8 @@ remains intact (see figure below). Spiking activity (top) and distributions of excitatory synaptic weights (bottom) for the network with integrate-and-fire (``iaf_psc_alpha_nest``) and :doc:`ignore_and_fire ` dynamics (``ignore_and_fire``). Figures -generated using :doc:`code/generate_reference_data-ignore_and_fire.py ` -and :doc:`code generate_reference_figuresi-ignore_and_fire.py `. +generated using :doc:`generate_reference_data-ignore_and_fire.py ` +and :doc:`generate_reference_figures-ignore_and_fire.py `. Scaling experiments @@ -113,8 +113,8 @@ You can run the simulations by using the provided Snakefile: * :doc:`Detailed description of the network model and parameters ` * :doc:`NEST simulation details ` - * :doc:`model.py `: NEST implementation of the network model - * :doc:`parameter_dicts.py `: parameter setting + * :doc:`model-ignore_and_fire.py `: NEST implementation of the network model + * :doc:`parameter_dicts-ignore_and_fire.py `: parameter setting * :doc:`ignore_and_fire model documentation `

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